Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
Authors: Derong Kong
Zhixiong Yang
Shengxi Li
Shuaifeng Zhi
Li Liu
Zhen Liu
Jingyuan Xia
Venue: NeurIPS 2025 poster ()
Keywords: Low-light image enhancement
Luminance-aware statistical quantization
Hierarchical luminance adaptation
Diffusion model.
Primary area: deep_learning

Abstract: Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing methods predominantly focus on deterministic pixel-level mappings between paired low/normal-light images, they often neglect the continuous physical process of luminance transitions in real-world environments, leading to performance drop when normal-light references are unavailable. Inspired by empirical analysis of natural luminance dynamics revealing power-law distributed intensity transitions, this paper introduces Luminance-Aware Statistical Quantification (LASQ), a novel framework that reformulates LLIE as a statistical sampling process over hierarchical luminance distributions. Our LASQ re-conceptualizes luminance transition as a power-law distribution in intensity coordinate space that can be approximated by stratified power functions, therefore, replacing deterministic mappings with probabilistic sampling over continuous luminance layers. A diffusion forward process is designed to autonomously discover optimal transition paths between luminance layers, achieving unsupervised distribution emulation without normal-light references. 
In this way, it considerably improves the performance in practical situations, enabling more adaptable and versatile light restoration. This framework is also readily applicable to cases with normal-light references, where it achieves superior performance on domain-specific datasets alongside better generalization-ability across non-reference datasets. The code is available at: https://github.com/XYLGroup/LASQ.

Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
Derong Kong' Zhixiong Yang'
Shengxi Li Jingyuan Xia
Shuaifeng Zhi²
Liu Zhen Liu'
College of Electronic Science and Technology, National University of Defense Technology College of Electronic and Information Engineering, Beihang University
Abstract
Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing meth- ods predominantly focus on deterministic pixel-level mappings between paired low/normal-lige images, they often neglect the continuous physical process of luminance transitions in real-world environments, leading to performance drop when normal-light references are unavailable. Inspired by empirical analysis of natural luminance dynamics revealing power-law distributed intensity transi- tions, this paper introduces Luminance-ANe Aware Statistical Quantification (LASQ), a novel framework that reformulates LLIE as statistical sampling process over hierarchical luminance distributions. Our LASQ re-conceptualizes luminance transition as a power-law distribution in intensity coordinate space that can be approximated by stratified power functions, therefore, replacing deterministic map- pings with probabilistic sampling over continuous luminance layers. A diffusion forward process is designed to autonomously discover optimal transition paths between luminance layers, achieving unsupervised distribution emulation without normal-light references. this way, considerably improves the performance in practical situations, enabling more adaptable and versatile light restoration. This framework is also readily applicable to cases with normal-light references, where it achieves superior performance better generalizaiion-ability across non-reference datasets. The code is available at: ftteps://githee htt.tau...ooomooooomaaaaaaa.eee. http//YYYGroop/L/.
datasets alongside
Introduction
In low-light environments, images frequently experience degradations like reduced visibility and heightened noise, which hinder subsequent vision-related tasks 2 3 4 Low-light image en- hancement (LLIE) aims to reconstruct perceptually natural scenes by establishing mappings between low-light and normal-light distributions. However, this problem is fundamentally ill-posed, natural luminance transitions follow continuous physical processes governed by scene radiance and sensor responses, rather than discrete pixel-level correspondences.
While recent deep learning methods whether supervised ( 6
7 8 9) unsupervised (10)
12) attempt to model light variations through paired or unpaired training, they inherently overfit static relationships between low/normal-light domains. Supervised methods rely on pixel-level correspondences in paired data, forcing models to prioritize localized correlations over the physics of gradual luminance evolution. Unpaired approaches, though avoiding direct pairing, still depend
Derong Kong and Zhixiong Yang contributed equally this work (' Corresponding author: Jingyuan Xia).
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

==1
n=
Figure 1: The physics-driven regularity of luminance intensity evolution.
pseudo-references derived from empirical gamma corrections (10), inheriting prior biases. Both paradigms oversimplify the inherently context-dependent and continuous nature of luminance dynam- ics, resulting in constrained generalization: models excel in domain-specifet scenarios with reference normal-light samples but struggle to adapt to unseen environments or sensor-specific degradations (I3 [4). This underscores the necessity for a paradigm shift toward learning luminance transitions from the intrinsic continuity of real-world illumination processes.
This work is motivated by empirical revelation: natural luminance transitions between low-light and normal-light conditions inherently adhere to power-law density distributions across hicrarchical intensity coordinates, visualized in Fig. [ Unlike the black-box mappings Iearned by existing methods, these distributions reveal a physics ified luminance layers governed by cumulative power functions (Fig. [.. Specifically, each layer corresponds to a distinct power-law parameter that dictates how localized or global the luminance adaptation should be. For instance, single power function approximates uniform luminance adjust- ment across the entire image (e.g., global gamma correction). while multiple overlapping functions capture spatially varying transitions, mimicking the interplay of scene radiance and sensor responses. By parameterizing these layers through variable-density sampling, where the number of power functions determines the granularity of luminance adaptation, we bridge the gap between pixel-level fidelity and cross- scess-sc generalization. Denser sampling (more functions) prioritizes localized intensity corrections resembling pixel-wise mappings, whereas sparser sampling (fewer functions) enforces smoother, physically consistent transitions across regions. This hierarchical decomposition fundamentally redefines LLIE: instead of deterministic low-to-normal mappings, we model LLIEE as a statistically driven process that progressively traverses luminance layers, emulating the continuous and context-aware nature of real-world illumination dynamics.
regularity-pixce intensities evolve along strat-
In this instance, we propose a luminance-aware statistical framework named Luminance- Aware Statistical Quantization (LASQ) that translates the hierarchical power-law distributions of natural illumination into an adaptive statistical sampling process. We first formulate a scale-adaptive luminance intensity estimation function governed by power-law exponents, where both the base and exponent are dynamically computed from localized intensity statistics across adjustable regions of the luminance map. This function derived from power-law regularity observed natural scenes, enabling seamless computation for pixel-level corrections to global adjustments luminance adaptation operators. On the basis of this, we formulate a distribution space for luminance adaptation operators that spans granularity levels-rangee specific refinements. A Markov Chain Monte Carlo (MCMC) sampling strategy then designed to progressively explore this space, initiating from global equilibrium states and iteratively introducing spatially varying layers to simulate the continuum of real-world luminance transitions. The sampled operators follow Gaussian-lik distribution, where highabrobability candidates correspond to
from coarse, scene-wide adjustments to fine- ane-geri region-

---

physically plausible global adaptations, while low- cww-oobbabllity ones represent localized refinements. This naturally reflects the rarity of extreme, pixel-level corrections in natural illumination transitions.
We note that this sampling mechanism is embedded into the forward process of a diffusion model, which learns traverse luminance layers in an unsupervised manner. By aligning the diffusion trajec- tory with the hierarchical granularity of luminance adjustments, our framework emulates the gradual, across-scene robust propagation of light real environments, achieving addelty-generrllizttee librium through statistically grounded layer-wise enhancement. The proposed LASQ framework thereby attains an optimal balance between local reconstruction precision and global robustness across diverse scenarios, eliminating the need for formal-light reference acquisition. Extensive experiments validate that LASQ. when integrated with vanilla diffusion model, achieves state-of-the offtthh-artt perfor- mance on non non-reference datasets while attaining comparable performance to reference-depente dependent methods on normal-ligh benchmark datasets. Furthermore, LASQ exhibits versatile compatibility: it seamlessly adapts to scenarios where normal-light references are available, delivering superior domain-specific enhancement alongside unparalleled cross-dataset generalization capabilities.
equi-
Our main contributions are summarized as follows:
We propose LASQ that fundamentally redefines LLIE by establishing the first physics-aware statistical model grounded in hierarchical power-law luminance transitions. This innovation bridges the gap between physical regularity modeling and data-driven learning paradigms, shifting the LLIE paradigm from deterministic pixel-wise mappings to stochastic processes governed by natural illumination statistics.
We establish statistical sampling on hierarchical luminance adaptation operator to emulate illumination transitions, where multi-scale power-law distributions are systematically param- eterized and sampled via adaptive MCMC strategies. This enables automatic adaptation from global equilibrium adjustments to localized refinements based on scene-based brightness characteristics.
We introduce diffusion-driven learning architecture that systematically incorporates phys- ical illumination priors through progressive luminance layer traversal during the forward diffusion process. This design enables unsupervised hierarchical enhancement while achiev- ing dual-mode compatibility with both reference- based and reference-free scenarios. thereby eliminating dependency on paired reference data.
Comprehensive experiments show that LASQ achieves i) superior performance on non- reference datasets without any reference guidance, ii) attains comparable performance to reference-basee methods on reference--vailable benchmarks even when references are withheld, and iii) outperforms existing reference-basee approaches when references utilized.
Related Work
2.1 LLIE via Pixel-Level Consistency
Contemporary approaches focusing pixel-level consistency be categorized into three evolution- ary stages 15 16
(2 2T). Early works like L.LNet (22) and Retinex-Net (23) leveraged paired datasets to train CNNs with pixel-wise losses, achieving precise local corrections but suffering from domain overfitting. Methods like EnlightenGAN (24) introduced cycle-consistency constraints, while Zero-DCE (25) used non- non-paired training with empirical illumination curves, both inheriting biases from heuristic priors (). Recent diffusion models (10) enhanced flexibility through noise-to- clean transitions, and FeatEnHancer (1) proposed hierarchical feature fusion to bridge pixel-Ievel and semantic gaps. These methods improved generalization but remained black-box mappings (3). While progressively reducing dependency on strict pixel correspondences, these methods universally prioritize pixel-wise fidelity over the continuous, context-dependent nature of luminance transitions, resulting in constrained generalization when facing unseen scenarios or sensor-spechs -peeofc degradations (13).
17,
18:
19
3

---

Statistical Sampling Process
MCMC Sampling
Luminance
Iuw
Map Extractor
Gp
yullirane Te.th
Optional Requued
Real?
Fake?
References
4
Encoder
Discriminator
F
DC
Zx
Decader
Figure 2: The framework of our LASQ.
LLIE with Illumination Priors
To alleviate these issues, several studies 26 27 25 have incorporated illumination-awwe aware correction into deep neural networks, framing LLIE as curve-estimation problem through different gamma correction samples. Specifically, (27) introduced learnable gamma transforms for illumination adjustment, yet enforced uniform corrections across all pixels. Methods like KinD (26) decomposed images into cilumination-reflettaaee components, but relied on manually designed reflectance priors that oversimplified real-world radiance interactions, and its reliance on downstream pre-training objectives (e.g., perceptual losses) further introduces external biases that impair adaptability to diverse degradation patterns. More latest works like LifhtenDiffusion (IO) integrated Retinex theory into diffusion steps, while GPP-LLIE (28) embedded illumination gradients diffusion guidance. Though enhancing physical plausibility, these methods still imposed global correction strategies through rigid equation constraints.
Current physics-aware approaches generally ignore the hierarchical power-law distributions that dictate natural luminance changes. These methods, which depend on global gamma-like adjustments or empirical reflectance models, do not adequately represent the layered luminance levels where both localized and global adjustments interact, thereby restricting adaptability to sensor-specific issues and real-world lighting variations.
3 Methodology
3.1 Notation
The overall framework of our LASQ is illustrated in Fig. 2 At its core, we denote low-light image by Σ, E (0, x and normal-light counterpart by In E [0, H and for each pixel
index i {1,..., I} with I H W, write the luminance pair s; ( I!t) Besides, P indicates the image region. We denote the luminance map by Gp and define 7/p as our hierarchical luminance adaptation operator. and represent the hierarchical enhanced image set and latent representations, respectively. Let E and 'D denote the encoder and the decoder. Go and D express the generator and the discriminator.
3.2 Hierarchical Luminance Modeling
Luminance Variation Coordinate System: In preparation for our statistical modeling low-light image enhancement, present unified two-dimensional coordinate system designed to map the

---

relationship between tnormal-light and "low-light" luminance intensities. Let In E [0. 1] "| denote an observed low-light image and IN E [0, "] be its normal-light light counterpart. For each element located at (zi, Vi), we write I II (2t, V:) and I = IN (Zi, Ui), = 1.... I, where as point 8; in the plane, we form the Luminance
I= H x W. By treating each pair [ I (i) N
Variation (LV) coordinate system:
{s - (1 i=1
Empirical observation reveals that, within the normalized range [0. 1], the low-light intensities exhibit a heavy-tailed power-law distribution, which can be approximated by a set of power-law functions
az" x E [0, 1], > 0, O,
where 6 denotes to the illumination change level by this power-law curve. As depicted in the fourth column of Fig. T the asymmetric distribution of underexposed elements can be approximated by set of sampled power-law curves, providing a rigorous foundation for adjusting the low-light distribution specified target using mapping or sampling methods.
Statistical Sampling Process:
Utilizing the LV coordinate system, we initially introduce the regional luminance scalar Gp for any designated image area P S [1, H x [1, W.. This scalar encapsulates the characteristic distribution of luminance within the region, and its precise formulation is provided in the Appendix. Utilizing Eq.
2 we proceed to compute our hierarchical luminance adaptation
operator (LAO) "pp as follows:
6e GC +
PP = (a+ Gr)
= =p - 11+7
(3)
where a E (0, 1]. 17, 8 are hyper-parameterr that control adjustment strength and contrast gain. Empirical analysis reveals distinet luminance adaptation patterns: single LAO exhibits uniform global luminance modulation, while multi-LAO configurations enable region-specific refinement. This phenomenon emerges because curves in the central regime of the power-law distribution (highlighted in red in Fig. (2) demonstrate universal traversal across all LAO set cardinalities, whereas boundary regions (depicted in blue) are exclusively accessible to high-density LAO sets implementing fine- grained adjustments. We consequently model this operator distribution through a symmetrically truncated Gaussian distribution (Fig. top center) follows:
p(>) xx exp
(7 - (7-20)2
E [immnn Finax],
where Ymin min;,j T(..j) 7max max;, Mi.j) and %0 HW Σ H1
ΣØ= 7(5) The bounded distribution can be formally expressed as y ~ Nunne (p = %0. ;²; Ymin, Tmaxx)
W
Building upon the symmetrically truncated Gaussian distribution p(7). we devise hierarchical Markov Chain Monte Carlo (MCMC) sampling scheme to generate LAO sets = [T>) where each iteration n produces 2n distinct LAO configurations via adaptive chain transitions, referring I, {
1
(x) PP ==1
The MCMC process at the n-th iteration given by:
P( ( = D(ZHH (n) ( IT)(((T)dT, Σ
P(I (1) hpp (p(1
(5)
derived from the continuous formulation through discrete sampling approximation. Each trial constructs a Markov chain defined by the transition kernel:
(n) q (7 1/.1-1)
Nunne (7%%) (())
7/ X2; Ymins Tnax
(6)
where the step size X adaptively balances pelloratio-eexplitatiii trade-off's across hierarchy levels.
The dynamically partitioned grid strategy ensures progressive refinement: at iteration n, the image is divided into m, X W,, non-overlapping patches (m,, = (x)-11 w,, 2 (m-1//2), This induces hierarchical luminance-corrected images H = {I(f)) N Jn=1, where each I((n) encapsulates 2 locally optimized gamma correction patterns. Crucially, every MCMC trial synthesizes a self-consistent LAO set that traverses luminance hierarchies through state-dependent transitions, enabling coarse-to-fin representation learning where global brightness constraints guide local refinements and vice versa.
5

---

3.3 Hierarchically-Guidee Diffusion
Forward Process with Hierarchical Guidance: The sampled set ( j.v- employs stochas- tic learning via diffusion transitions, exploiting the Markov property-eace I(n) relies only on its forerunner-to adaptively direct noise injection. The low-light image I, and H. are encoded together using { incorporating kt residual blocks and max-pooling for latent features Fr. E IR 2 x xc
and [(() E N' W. XC We align the T Ttep diffusion with { {-(²)
using a temporal
x=1 mapping & (I,...,, T fL...., N S T. by (tt) = =[t N//T], such that:
n=1
N..
Tal. tE 3y F F1 (0))
The forward diffusion adds Gaussian noise progressively:
(< = Maxa; v 1,
(8)
where E {L....,T} denotes the diffusion timestep, 4 represents the random variable timestep and Br denotes the noise variance. Accordingly, for each temporal interval Tn, the corresponding spatial variant FH (+)) is utilized as the illumination normalization reference, thereby maintaining suminance-consisten forward sampling. By incrementally incorporating spatial luminance awareness from coarse fine scales into the diffusion forward trajectory. the model acquires multi-level representation of illumination dynamics. This hierarchical perception facilitates adaptive noise scheduling and enhances robustness across a wide range of lighting conditions.
Hierarchically-Guidee Diffusion Denoising: During the reverse training phase, the denoising network co (I2,, Fr.) is trained achieve:
3r (x+ Bico (Ir,t,FF)) + oyb, b~0,0,,
F2-1 VI
where denotes the standard deviation. Based the variational lower bound of the forward process, we minimize the mean squared error between the true noise and the network prediction, leading to the simplified noise prediction objective:
T
La
Σ By(
[lke co (
(10)
where e represents the actual injected noise. To ensure overall image smoothness and preserve fine details while minimizing generation artifacts, employ the global label F (()()) weakly guide the reverse diffusion process:
Cg -
D(F (()))
(11)
where the (-)) denotes the decoder. During inference, under the guidance of the low-light input FL. employ the diffusiin model's implicit sampling strategy (29) for reverse denoising. The model utilizes its learned distribution fit the optimal illumintioon-enhane which is then decoded to yield the final output IN >D(Fx)
feature representation Fx.
The LASQ framework can integrate effortlessly with normal-light references. In this setup, an optional adversarial discriminator D& complements the LASQ, creating hybrid diffusion-GAN model. The generator's training involves a combined loss:
Liotal = AgLa + Aghe + AGAN Ez, [- log DG (IL)))
(12)
adversarial penalty
where adversarial training refines high-level textures while preserving the physical grounding from diffusion priors. These results are presented with ISAQ++ in the simulation. Implementation details for reference-augmente training provided in the Appendix.

---

Sageet
(b) Bata
KaDD
Get KcBCs
10 PusLiE
SCL-
Figure 3: Qualitative comparison of our method and competitive methods on the LOLv1 and LSRW test sets. "LASQ++" denotes the incorporation of unpaired normal-ligh references.
4 Experiments
4.1 Experimental Settings
All experiments are carried out on a cluster of four NVIDIA A800 GPUs under Python 3.9 and Py PyTorch 2.0, with fixed batch size of 16. We employ the Adam optimizer (30), setting the denoising diffusion process learning rate x 10-5, while using a sampling ratio k The hyperparameters Aa, Ag and AGAN (if activated) are set to 0.9, 0.005 and 0.7 respectively. Noise estimation during diffusion training performed using the U-Net (31) architecture with T 1000 time steps.
4.2 Datasets and Metrics
We validated our approach using both paired and unpaired low-light benchmarks. For the paired evaluation, we used the LOLvI (22) and LSRW (32) test sets-each comprising matched low- and normal-illuminaton image pairs aadrs-and reported restoration fidelity via PSNR and SSIM (33). alongside the full-reference perceptual score LPIPS (34). To assess performance the absence of ground- truth references, we then tested on the unpaired LIME (35), DICM (36), NPE (37). and VV (38) collections, measuring perceptual quality with the no-reference NIQE and 44)) metrics. Our comparative study encompasses six supervised approaches (RetinexNet 22 KinD++ (23).
LCDPNet (21). URetinexNet (41), SMG (17) and PyDiff (42)) and six unsupervised approaches
(Zero-DCE (25). EnlightenGAN (24), SCI (19), PairLIE (43) SCL-LLE 44). LightenDiffusion (10)
and NeRCo 18)
4.3
Qualitative Comparison
As shown in Fig. 3 LASQ achieves enhanced local brightness adaptation and superior detail fidelity comparable supervised methods URetinexNet (4I) and KinD++ (23) on ground truth- annotated datasets, while demonstrating improved domain-adaptive color reproduction through integration with unpaired normal-light images in LASQ++. By contrast, existing methods exhibit distinct limitations: SCI (19) and SCL-LLE 44 suffer from persistent underexposure, whereas EnlightenGAN (24) produces blurred structural details, and NeRCo (I8) tends to generate localized over-exposure artifacts. Furthermore, Fig. 4 confirms LASQ's S exceptional performance in real-world scenarios through its complete avoidance local overexposure, noise amplification, and artifacts that persistently affect other methods: EnlightenGAN (24), NeRCo (88 and PairL (43) notably show severe localized overexposure and lens flare artifacts, and even supervised approaches URetinexNet (41) and KinD++ (23) still struggle to fully suppress these issues. Crucially, LASQ maintains natural
7

---

B
Exk
No Unverdd Whrnk
Truch Boal largee
1000
SCU-LUB
Figure 4: Qualitative comparison of our method and competitive methods the LIME, and VV datasets. More results will be provided the Appendix.
scene characteristics without compromising detail fidelity or color consistency, thereby demonstrating unprecedented cross-scenario generalization capability across both constrained laboratory settings and unconstrained environmental conditions. This comprehensive evaluation systematically validates LASQ's technical superiority in terms of adaptive illumination control, artifact suppression, and domain transfer effectiveness. More results will be provided in the Appendix.
4.4 Quantitative Comparison
The quantitative evaluation results across diverse datasets are summarized in Table where LASQ demonstrates performance parity with leading supervised techniques on LOLvI (22) and LSRW (32)
while achieving state-of-the-art results among unsupervised methods through integration of unpaired normal-light images. Notably, on datasets DICM (36), NPE (37), and VV (38), LASQ outperforms existing approaches across most perceptual metrics, thereby confirming its intrinsic generalization prowess without domain-specific adaptation. Although normal-light reference integration improves color fidelity, its tendency toward overfitting partially counteracts the model's 's inherent generalization capacity, resulting metric degradation LASQ++. Crucially, this performance highlights LASQ's fundamental advantage in balancing domain adaptation with cross-scenario robustness, whereas LASQ++ prioritizes target-domain color accuracy at the expense of slight generalization capability. The extended experimental results, including comprehensive qualitative analyses and quantitative evaluations, are provided in the Appendix.
4.5
Computational Cost
We show the computational complexity metrics in Table T (NVIDIA A800, LOLv1 dataset). The coarsc-to-fine MCMC sampling mechanism is only used during training and embedded into the forward diffusion process. It guides the model to traverse luminance layers in a hierarchical manner, enabling structured learning of light propagation. During inference, our model only performs the denoising step conditioned on the low-light input within the diffusion model, which is significantly more efficient.
We compare LASQ against both early non non-diffusion--arr methods (e.g., EnlightenGAN, KinD++), and recent diffusion-based approaches (e.g., WCDM, LightenDiffusion), While the early methods are lightweight, their performance lags far behind diffusion-based models across all key metrics. Existing diffusion models, although significantly more effective, tend to suffer from high computational cost due deep architectures and iterative sampling. LASQ, while maintaining the performance

---

Table 1: The quantitative comparison results of partial experiments, with the best-performingg results marked red and the second-best blue. The notations "SL." and "UL." respectively represent supervised and unsupervised learning approaches. "LASQ++" denotes the incorporation of unpaired normal-ligtt light references
Type
Mathod
LOLvl
LSRW
DICM
NPE
VV
PSNKT
SSIMT
LPIPS.
PSNRT
SSIM
LPIPS)
NIQEL
PLC
NIQEJ
PL
NIQEJ
PIJ
RetinexNet
16.774
3.242
4.732
3.727
KinDa+
17.752
0.758
0.198
16.085
0.394
0.366
4.027
3.999
4.005
3.144
3.586
2.773
LCDPNet
14.506
0.575
0.312
15.05 15.089
0.474
0.314
1.110
3.250
4.126
3.127
5.039
3.3417
SL
URetinexNet
19.842
0.824
0.128
18.271
0.518
0.295
4.774
3.565
4.028
3.153
3.851
2.891
SMG
23.814
0.809
0.144
17.579
0.338
0.456
6.224
4.228
5.300
3.627
5.752
3.737
PyDiff
23.275
0.859
0.108
17.264
0.510
0.335
4.499
3.792
4.082
3.268
4.360
3.678
Zero-DCE
14.861
0.562
0,330
15.867
0.443
0.315
3.951
3.149
3.826
2.918
5.080
3.307
EaGAN
17.606
0.653
0.319
17.106
0.463
0.322
3.832
3.256
3.775
2.953
3.689
2.749
SCI
14.784
0.525
0.333
15.242
0.419
0.321
4.519
3,700
4.124
3.534
5.312
3.648
PairLIEE
19.514
0.731
0.254
17.602
0.501
0.323
4.282
3.469
4.661
3.543
3.373
2.734
UL
SCL-LLE
10.754
0.506
0.382
13.110
0.310
0.396
5.129
3,809
4.873
3.692
5.513
4.316
NeRCo
19.738
0.535
3.345
3.902
3.037
LigDiff
20.453
0.803
0. 192
18.565
0.539
0.311
3.724
3.144
3.618
2.879
2.941
2.558
LASQ
20.375
0.814
0.191
18. 137
0.347
0.308
3. 715
3.128
3.571
2.764
2.777
2.623
LASQ++
28.481
0.807
0.205
58.
0.340
0.316
3.723
3.137
3.601
2.789
2.850
2.691
Table 2: Comparison of computational efficiency and resource usage.
Method
FLOPs (G)
Params (M)
Inference Time (ms)
Memory Usage (MB)
EnligheenCAAN
70.16
241.48
KinD++
17.49
8.27
4279.70
372.19
354.77
2320.87
PairLIE
81.84
0.34
900.70
3499.79
SCI
0.13
50.14
20.01
SCL-LLE
19.01
0.08
60.59
324.31
URetinex
81.35
0.34
129.70
568.48
WCDM
206.66
6017.86
LightenDiflusion
367.99
27.83
257.94
8049.95
LASQ
24.08
6496.68
advantages diffusion models, achieves inference efficiency comparable to non-diffusion-base methods. This makes highly suitable for real-world deployment. Considering the substantial performance gain without reliance on reference images, the moderate computational overhead of LASQ is practical and acceptable trade-off.
Ablation
5.1
Fixed Luminance Adjustment
We replace our adaptive MCMC-based luminance adaptation with static, hand-crafted functions (e.g.. global gamma correction or fixed tone curves) drawn from prior LLIE methods (45
46 47 48).
Embedding these into the forward diffusion degrades PSNR, SSIM, and LPIPS (Table 3 showing reduced feature richness and generative fidelity (Fig. 5). In contrast, our physics-driven. multi-scale operator-vis plwer-law stratification and adaptive sampling- bettein balances global consistency and local detail, yielding stronger cross-scenario generalization and perceptual quality.
5.2 Limited Hierarchy
We keep adaptive MCMC sampling but limit the operator to two layers: global adjustment and per-pixel correction, omitting mid-level power-law strata. This two-layer variant still beats the fixed baseline, yet its PSNR, SSIM, and perceptual metrics fall short of the full LASQ (Table 3 confirming that intermediate layers are essential for smoothly refining illumination and preserving both quantitative performance and visual fidelity.

---

(a) luput
(b) FLA
(c) LHI
(a) LASQ
(e) LASQ +++
Figure 5: The qualitative results of ablation studies.
Table 3: Quantitative results of ablation studies. The "FLA" and "LH" respectively represent the
ablation of Fixed Luminance Adjustment and Limited Hierarchy.
Method
LOLv1
LSRW
DICM
NPE
VV
PSNRT
SSIMT
LPIPS.
PSNRT
SSIM
LPIPS.
NIQEL
PLL
NIQE]
PL.
NIQEI
PLJ
16.741
0.399
4.265
3.529
3.007
19.139
0.792
0.243
18.026
0.522
0.333
3.759
3.306
3.648
2.996
3.006
2.730
LASQ
20.375
0.814
0.191
137
0.547
0.308
3.715
3.128
3.571
2.764
2.777
2.623
LASQ+
20.481
0.807
0.205
18.584
0.541
0.316
3.723
3.137
3,601
2.789
2.850
2.691
5.3 Hyperparameter Sensitivity
As illustrated in the table, we systematically varied key hyperparameters including o, 7), As and Xg over range of values Bp is determined by ). The results show that while performance slightly fluctuates with different settings, the overall impact on metrics remains moderate. For instance, varying between 0.05 and 0.6 only causes minor PSNR change (within 0.3 dB) and negligible shifts perceptual scores. Similarly, other hyperparameters demonstrate stable trend without sharp degradation. These experiments demonstrate that our method is not overly sensitive to hyperparameter selection, and maintains consistently strong performance across broad range of Bettings-highlighee its robustness, practical stability, and generalization potential.
Table 4: Ablation study on key hyperparameters, The best results for are highlighted bold.
Param
Value
PSNRT
LPIPS)
SSIM
0.05/0.1/033/06
0.332/ 0.32000..03.. /.3324
0.512/0.543
0.1/
17.85 18.35
18.17
1 17.95
0.335/0.321
/0.324/0.329
0.537 /0.543
0.546 0.540 0..53/
Xa
0.1/ 20
17.82/
18.04
17.85
0.324
0.545
0.001 /.0005//0000000 0.01 /0.1
17.76/
18.22/
18.16
17.88
0.312/0330/ 0.331
0.536 0.548
0.547 0.548/0.5477
0.544
Conclusion
The proposed LASQ framework reorients the LLIE challenge by merging illumination continuity physics with deep-learning, moving beyond conventional pixel-level methods. redefines L.IE as continuous stochastic task using adaptive MCMC, capturing real illumination through layered luminance analysis. Transitioning from reliance on paired data to unsupervised layer exploration. it improves generalization and can support reference contexts. It balances overall illumination with local detail, effectively resolving fidelity vs. adaptability issues, while avoiding gamma-relater biases and overfitting of supervised methods. These advancements underscore broader insight: LLIE must evolve from pixel-wise function approximation to spatiotemporal reconstruction of light dynamics. Future work should explore dynamic plwer-law parameterization for time-varying scenes and hardware-softwar
co-design to align statistical priors with sensor-specific noise profiles. Narrowing these gaps will enhance computational imaging systems to better mimic biological vision in low light.
10

---

Acknowledgments and Disclosure of Funding
This work is supported by the National Natural Science Foundation of China under Grant 62576350, 62131020, 62376283 and 62531026.
References
[T] I.. Ye and Ma, "L.lod: a object detection method under low-light condition by feature enhancement and fusion," in 2023 4th international seminar artificial intelligence, networking and information technology (AINIT). IEEE, 2023, pp. 659-662.
[2] Z. Li, Li. Niu, C. Rong, and Wang. "Infrared and visible light fusion for object detection with low-light enhancement," in 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024, pp. 120-124.
[3] L. Xiong, X. Feng, L. Zhang, Q. Sun, and H. Ren, "Semantic segmentation algorithm of nighttime road surface water based on improved low-light image enhancement," in 2024 5th International Conference on Intelligent Computing and Hluman-Commpenn Interaction (ICHCI). IEEE, 2024, pp. 104-108.
[4] Z. Lu, H. Sun, L. Lei, Y. Xu, Y. Sun, and G. Kuang, "Diffdual-ad: Diffusion-basee dual-stage adversarial defense framework in remote sensing with denoiser constraint," IEEE Transactions on Geoscience and Remote Sensing, 2025.
[5] X. Yi, H. Xu, H. Zhang, L. Tang, and Ma, "Diff-retinex: Rethinking low-light image enhancement with generative diffusion model," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 122--1131..
[6] Y. Cai, H. Bian, Lin, H. Wang, R. Timofte, and Y. Zhang, "Retinexformer. One-stage vetinex-based transformer for low-light image enhancement," in Proceedings of the IEEF/CVF international conference on computer vision, 2023, pp. 12 504-1155...
[7] Lv, Lu, J. Wu, and C. Lim, "Mbllen: Low-light image/video enhancement using cnns." in Bivc, vol. 220, no. 1. Northumbria University, 2018, p. 4.
[8] I.. Shen, Z. Yue, F. Feng, Q. Chen, S. Liu, and J. Ma, "Msr-net: Low-light image enhancement using deep convolutional network," arXiv preprint /711: 2017.
[9] F. Yam and Z. Hassan, "Innovative advances in led technology." Microelectronics Journal, vol. 36, no. 2, pp. 129-137, 2005.
[10] H. Jiang, A. Luo, X. Liu, Han, and S. Liu, "Lightendiffusion: Unsupervised low-light image enhancement with latent-retinex diffusion models," in European Conference Computer Vision. Springer, 2024, pp. 161-179.
[11] Yang. Zhang, Y. Wang, Yu, Y. Wang. and Zhang, "Difflle: Diffusion-guided domain calibration for unsupervised low-light image enhancement," arXiv preprint a:Xiv:2308.09229, 2023.
[12) Y. Shi, D. Liu, L.. Zhang, Y. Tian, X. Xia, and X. Fu, "Zero-ig: zero-shot illumination- guided joint denoising and adaptive enhancement for low-light images," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 3015-3024.
[13] S. Tu, W. Yang, and B. Fei, "Taming generative diffusion prior for universal blind image restoration," Advances in Neural Information Processing Systems, vol. 37, pp. 21 172-21 206, 2024.
[14] J. Hou, Z. Zhu. J. Hou, H. Liu, H. Zeng, and H. Yuan, "Global structure-aware diffusion process for low-light image enhancement," Advances in Neural Information Processing Systems, vol. 36, pp. 79 734-799777 747, 2023.
[15] Y. Wu, C. Pan, G. Wang, Y. Yang, J. Wei, C. Li, and H. T. Shen, "Learning semantic-aware knowledge guidance for low-light image enhancement," in Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, 2023, pp. 1662-1671.

---

[16] X. Xu, R. Wang, C.-W. Fu, and J. Jia, "Snr-aware low-light image enhancement." in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 17 714- 17 724..
[17] X. Xu, R. Wang, and J. Lu, "Low-light image enhancement via structure modeling and guidance," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 9893-9903.
[18] S. Yang, M. Ding, Y. Wu, Z. Li, and J. Zhang, "Implicit neural representation for cooperative low-light image enhancement," in Proceedings of the IEFF/CVE international conference on computer vision, 2023, pp. 12918-12927.
[19] L. Ma. T. Ma, R. Liu, X. Fan, and Z. Luo, "Toward fast, flexible, and robust low-light image enhancement," in Proceedings of the IEEE/CVF conference computer vision and pattern recognition, 2022, pp. 5637-5646.
[20] R. Liu, L. Ma. J. Zhang. X. Fan, and Z. Luo, deetinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement," in Proceedings of the IEEE/CVF conference computer vision and pattern recognition, 2021, pp. 00561-105700
[21] H. Wang. K. Xu, and R. W. Lau, "Local color distributions prior for image enhancement," in European conference on computer vision. Springer, 2022, pp. 343-359.
[22] C. Wei, W. Wang. W. Yang, and J. Liu, "Deep retinex decomposition for low-light enhancement," arXiv preprint arXiv: 100::088800000 2018
[23] Y. Zhang, X. Guo, J. Ma, W. Liu, and J. Zhang, "Beyond brightening low-light images," International Journal of Computer Vision, vol. 129, pp. 1013-1037, 2021.
[24] Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang. P. Zhou, and Z. Wang, "Enlightengan: Deep light enhancement without paired supervision," JEEE transactions image processing, vol. 30, pp. 2340-2344 2021.
[25] C. Guo, C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, "Zero-reference deep curve estimation for low-light image enhancement," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1780-1789.
[26] W. Wang, R. Luo, W. Yang, and J. Liu, "Unsupervised illumination adaptation for low-light vision," " IEEE Transactions on Pattrn Analysis & Machine Intelligence, no. 01, pp. 1-15, 2024.
[27] Y. Wang, Z. Liu, J. Liu, S. Xu, and S. Liu, "Low-light image enhancement with illumination- aware gamma correction and complete image modelling network," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 13 13 137.
[28] H. Zhou, W. Dong, X. Liu, Y. Zhang, G. Zhai, and Chen, "Low-light image enhancement via generative perceptual priors." in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 10, 2025. pp. 10 752-210 760.
[29] J. Song, C. Meng, and S. Ermon, "Denoising diffusion implicit models," arXiv preprint arXiv:2010002502. 2020.
[30] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint 11X1v:1412.6980, 2014.
[31] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical image computing and computer- assisted Intervention-MICC MICCAI 2015: I 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer, 2015, pp. 234-241.
[32] J. Hai, Z. Xuan, R. Yang, Y. Hao, F. Zou, F. Lin, and S. Han, "R2rnet: Low-light image enhancement via real-low to neal-normal network," Journal of Visual Communication and Image Representation, vol. 90, p. 103712, 2023.
12

---

[33] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity"" IEEE transactions on image processing, vol. 13, no. pp. 600-612, 2004.
[34] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, "The unreasonable effectiveness of deep features as a perceptual metrie," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586-595.
[35] X. Guo, Y. Li, and H. Ling, "Lime: Low-light image enhancement via illumination map estimation," " IEEE Transactions on image processing, vol. 26, no. 2, pp. 982-993, 2016.
[36] C. Lee, C. Lee, and C.-S. Kim, "Contrast enhancement based on layered difference representa- tion of 2d histograms," IEEE transactions on image processing, vol. 22, no. 12, pp. 5372-5384, 2013.
[37] S. Wang. J. Zheng, H.-M. Hu, and B. Li, "Naturalness preserved enhancement algorithm for non-uniform illumination images," " IEEE transactions on image processing, vol. 22, no. 9, pp. 3538, 2013.
[38] V. Vonikakis, R. Kouskouridas, and A. Gasteratos, "On the evaluation of illumination compen- sation algorithms," Multimedia Tools and Applications, vol. 77, pp. 921 1-9231, 2018.
[39] A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a "completely blind" image quality analyzer," IEEE Signal processing letters, vol. 20, pp. 209-212, 2012.
[40] Y. Blau, R. Mechrez, Timofte, T. Michaeli, and Zelnik-Manon "The 2018 pirm challenge on perceptual image super-resolution" in Proceedings of the European conference on computer vision (ECCV) workshops, 2018, pp. 0-0.
[41] W. Wu, Weng, P. Zhang, X. Wang, W. Yang, and Jiang, "Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 5901-5910.
[42] D. Zhou, Z. Yang, and Y. Yang, "Pyramid diffusion models for low-light image enhancement," arXiv preprint arXiv:2305. 10028, 2023.
[43] Z. Fu, Y. Yang, X. Tu, Y. Huang, X. Ding, and K.-K. Ma, "Learning a simple low-light image enhancer from paired low-light instances," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 22 252-22261.
[44] D. Liang, L. Li, M. Wei, S. Yang, L. Zhang. W. Yang. Y. Du, and H. Zhou, "Semantically contrastive learning for low-light image enhancement." in Proceedings of the AAAI conference on artificial intelligence, vol. 36, no. 2, 2022, pp. 1555-1563.
[45] S.-C. Huang, F-C. Cheng, and Y.-S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution." IEEE transactions image processing, vol. 22, no. 3, pp. 1032-1041, 2012.
[46] F. Kallel, M. Sahnoun, A. Ben Hamida, and K. Chtourou, "Ct scan contrast enhancement using singular value decomposition and adaptive gamma correction." Signal, Image and Video Processing, vol. 12, pp. 905-913, 2018.
[47] Z. Huang, T. Zhang, Q. Li, and H. Fang, "Adaptive gamma correction based on cumulative histogram for enhancing near- near-infrared images," " Infrared Physies & Technology, vol. 79, pp. 205- 215, 2016.
[48] W. Wang, X. Wu, X. Yuan, and Z. Gao, "An experiment- review of low-light image enhancement methods," leee Access, vol. 8, pp. 87 87884-8799 917, 2020.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: the abstract and introduction. we propose Luminance-Awaa Statistical Quantization (LASQ) physics-informed statistical framework for low-light image en- hancement, This framework is theoretically grounded in hierarchical power-law distributions of natural illumination (as formalized in Sec. (3 and experimentally validated through com- prehensive evaluations of both reference-based and reference-free scenarios (detailed Sec.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize other settings.
is fine include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The primary limitations of LASQ lie in its relatively high computational complexity and the requirement for manual hyperparameter design. Detailed discussions of these limitations are provided in the Appendix.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors encouraged to create separate "I "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
14

---

3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification: The variables of the proposed methodology have been formally defined in Sec. while extended mathematical derivations and rigorous proofs will be documented in the Appendix.
3
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
Justification: In implementation details, we give the parameter settings and network archi- tecture of our LASQ in Sec. [.1 Besides, the hyperparameter-tuee teneerr-ttnnngggnn provides the choice of our hyperparameters in the Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access hosted model (e.g., in the of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily the architecture clearly and fully.
model architecture, the paper should describe
(c) the contribution a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
15

---

(d)
We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification:
We will provide open access to our data and code upon acceptance.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips... cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https //nips.ccpputee wttpcccccppccccccee copollccggiieee wwwedeeeeeooebbbmeeeeeee
ssioonolicy for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
We provide the explicit experimental details. In Sec. 4. and Appendix, we provide the details of datasets, including the number, data splits. Besides, set the Adam optimizer to train our LASQ.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core the paper to a level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: We run the experiments for 5 times and report the average value.
Guidelines:
16

---

The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by enror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error that would yield results that are out range (e.g. negative error rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Our LASQ is trained on a cluster of four NVIDIA A800 GPUs. The detailed information about computer resources are given in Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification: Our research conducted in the paper conforms, in every respect, with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
17

---

Justification:
The impact of our LASQ has been discussed in the Appendix.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of MI.).
11. Safeguards
Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, scraped datasets)?
Answer: [NA]
Justification:
Our paper does not pose such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [NA]
Justification: Our paper does not use the existing above assets.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
18

---

The name of the license (e.g., CC-BY 4.0) should be included for each asset. For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the palaaets.paperssttt paperssi thcode. com con/datases package should be provided. For popular datasets,
has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is available online, the authors are encouraged reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: This paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification:
This paper not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: This paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
19

---

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if' applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification:
The LLMs is not the core methods in our research.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy for what should or should not be described.
20

---

A The Detailed LASQ Workflow
Luminance Variation Coordinate System: To systematically characterize low-light degradation, we establish the Luminance Variation (LV) coordinate system that geometrically encodes pixel-wise illumination relationships. Lr, E [0, =| and IN E [0, 1] xxx denote paired low/normal-light images. For each pixel we define its luminance state as:
{ (I/ IN
(13)
Power Law like Transformation: As visualized in Fig. (a). these coordinate points &; constitute geometric manifold where the horizontal/vertical axes respectively represent low/normal-light intensity spaces. This formulation provides two critical physical insights: 1) Each 8; encodes a pixel- specifie luminance attenuation pattern; 2) The points' spatial distribution reflects global illumination degradation statistics. The transition from Fig. (b) to (d) reveals our hierarchical modeling strategy. For an individual 8,, derive its local transformation strategy through:
y axx x E [0, 1], 0,0
(14)
where quantifies the exposure compensation magnitude at pixel (Fig. (b)). We note that exhibits spatial correlation with scene content requiring aggressive enhancement (e.g., shadows), while higher к preserves highlights.
A values (steeper curves) correspond to regions
Extending to multiple pixels (Fig. (e))), we observe heterogeneous curve families governed by parameter distributions {kp}. Each curve represents a distinct luminance adjustment strategy:
Highlight preservation (1)++)): Linear mapping maintaining high-intensity features.
Mid-tone enhancement (0.5<xx<<)> Concave curves amplifying moderate intensities.
Shadow recovery <Op):.))): Convex curves dramatically boosting dark regions.
(x
(c)
(4)
Figure 6: Luminance Variation Coordinate and the progressive modeling on full-domain luminance intensity elements via power law curves. (a) Luminance variation coordinate with pink background, where each sample 8, is geometrically determined by the horizontal low-light intensity I and vertical normal-light intensity z(() marked by central red dot; (b) Single-sample modeling (green background) demonstrating that the luminance transformation of individual elements follows red-colored power law curve; (c) Multi-sample extension (cyan background) showing diversified mapping relationships through color-coded power law curves corresponding different elements; (d) Full-domain fitting (purple background) achieved by dense overlapping curves that comprehensively cover the coordinate space.
Full-domain fitting in Fig. (d) employs dense overlapping curves to approximate the entire {s:) distribution. While achieving thcoretical completeness through
(((r,az")) |x E [o, 1]} + coordinate coverage,
(15)
i=1
this werrparameterized paradigm introduces critical limitations. First, optimizing curves the pixel level ignores spatial coherence, leading artifacts as adjacent pixels apply divergent enhancement
21

---

strategies. Second, estimating Kip becomes problematic in low-intensity areas CI) <0.1), where slight intensity shifts cause significant curve fluctuations. Lastly, focusing heavily on the individual {Kp} parameters greatly restricts practical implementation due to poor generalization. Therefore, we Sarc---amllinnbaaee physics-aware (Kp) prediction to preserves the LV system's 's statistical advantages while mitigating overfitting.
statistically formulate an
Hierarchical Sampling of Luminance Adaptation Operators: Given a low-light RGB image Ir E IR "x= x3, we first convert it into the YUV color space and perform guided filtering to compute the smoothed illumination map G(i,j), where (i.j) denotes pixel coordinates. The local linear coefficients in the guided filtering process can be formulated as:
oi(1,)) a(i.j) (1.5)
b(i,)) = pL(i.) a(i.)) ((i.)))a (L(j.),
(16)
where Jrr (i.j) represents the local window mean and (i. denotes the local window variance. Through coefficient smoothing, G(i.j) is obtained as:
G(i,j) - a(i,j) L(i.j) + b(i,j),
(17)
where à and b denote the mean-filtered results of a and b, respectively. This luminance map G(i.) j) is then aggregated over arbitrary image regions P S [1, H] [1, WI] to obtain the regional scalar Gp, which quantifies local luminance characteristics and serves as the input to compute the LAO ypp via the parameterized transformation introduced in the main text Section 3.2 Statistical Sampling Process.
Coarse-to- Hierarchy Gaussian distribution As illustrated in Fig 7 LAOs are sampled from a truncated Gaussian distribution Nimne (n = YYo a²: Timin: 7max)- - Commencing from a globally balanced state, spatially-varying layers are iteratively introduced to simulate the continuity of real- world luminance transformations. The sampling operator employs a truncated Gaussian distribution wherein phh-pprbbbbility candidates correspond to physically plausible global adaptations, while low-probability candidates represent local refinements. This characteristic inherently reflects the natural illumination transitions where extreme pixel-level modifications rarely occur. The transition between states is governed by a kernel that ensures smooth evolution across iterations:
(m) (70 111)-1 = NNrune (7p.2 11 X2: Yminn Tmax
(18)
This hierarchical sampling yields sequence of LAO sets r (Ta) with each I,, 2"
( (n) (Pps a=1
representing the spatially adaptive enhancement parameters. When applied, these
sets give rise to family of luminance-enhaneed images H. = (I(r) ) capturing coarse-to-fiee progression of illumination correction. This structure enables flexible exploration of global and local enhancement effects through state-dependent sampling transitions across the luminance field.
Hicrarchically-Guiee Diffusion: The forward process employs a hicrarchically-guided diffusion framework to progressively enhance low-light images through semantically-aligned noise injection. By encoding the input Σ, and its luminance-enhanced variants H into latent features Fr, and
(FIT) n=11 "n==' the method establishes temporal correspondence between diffusion steps and multi-
scale guidance via mapping function (t). This alignment dynamically steers the noise trajectory,
where each transition step incorporates hierarchical semantic shifts derived from Fil to modulate the degradation path. Throughout the forward pass, coarse-to-fine illumination characteristics are gradually imprinted into the diffusion process. During reverse denoising, the network eg iteratively removes noise while synergizing hierarchical cues from original low-light features Fz. The denoising trajectory is regularized by a dual objective: noise prediction loss ensures faithful reconstruction aligned with hicrarchical semantics, while a global constraint Lg enforces the structural consistency.
Adversarial Discriminator for Iterative Refinement: To optionally further enhance realism and when normal-light reference is available wee introduce discriminator D alongside our iettussoonbbbeee generator Gg. Given an unpaired sample Invenal ~ Poormal (if available) from the normal-light distribution, the discriminator learns to distinguish between true normal- light images and our enhanced outputs In GI().). We therefore cast the overall framework as hybrid
perceptual
22

---

(0) n
(0) 3
(e)
(4) m
Figure 7: The hierarchical MCMC sampling scheme to generate LAO sets. (a) Initial state (n 1)
with a uniform LAO operator (1) (red curve) globally adjusting pixel intensities via orthogonal projections fit shallow orange sample points (approximate power-law distribution); (b) Intermediate sampling (n = 3) generates three green curves (3) (7i=2,3,1) near the red curve, grounding on the Markov chain posterior; (c) Increased sampling times (n 4) introduces external blue curves (7,,8,6,788)
which entail power law functions (7)=5,s) that are outlying of the main distribution area; (d) Converged state (n = N) achieves pixel-level granularity through dense overlapping curves, dynamically fitting the orange distribution.
IiltusioonGAAN optimizing both the variational diffusion objective and an adversarial objective in an alternating fashion.
Concretely, at each training iteration t, perform the discriminator update
① - Φ To V Ladv (Doi Go), Ladv = R Paomel [log D Do EI, [log(1= - Do(Go(Zz)))):
(19)
and the generator update can be formulate as:
0 レ 8 no Vo (Luual AGAN LG-adv); LG-adv EI, log Po(Go(IL)))
(20)
where Né. 7le learning rates, and AGAN balances adversarial and diffusion losses. Therefore, the full generator loss becomes:
Lirtal daLa XpCx + AGAN Ex, [- log (G
(21)
adversarial penalty
By unifying physically grounded diffusion priors with adversarial discrimination, our hybrid frame- work yields outputs that not only preserve structural details and smooth lighting transitions but also exhibit the sharp textures and natural contrasts characteristic of true normal- light images.
During the inference phase, the model synthesizes an optimal enhanced representa- tion Frr that balances global illumination correction with local texture fidelity through implicit latent sampling guided by FL, starting from Gaussian random noise. This representation is subsequently decoded into the final output I through the learned mapping function.
Inference Stage:
B The Algorithmic Framework
Algorithm I summarizes the full LASQ (Luminance-Adaptatiin Sampling and Hierarchically-Gnn Guidedd Diffusion) workflow. It first extracts a latent representation via an encoder, then performs multi-scale luminance-adaptive sampling generate a hierarchy of intermediate images. These are used to guide both the forward and reverse diffusion processes, and finally decoder reconstructs the enhanced high-quality output, optionally applying adversarial training for further refinement.
23

---

Algorithm The LASQ pipeline.
1: Input: Low-light image It.
2: Hierarchical LAO sampling:
Initialize [ = 0. / =
4: Compute mean luminance Gp. then
5: ?>P = (a + G
6: for N...No do
7: Sample 7 ~ NNrue (70: o², 7min ?maxs
8: Tn { {7/02 2
2
Update 144))
end for
10: Collect H
1 (2)) J
11: Hierarchically-Guin diffusion:
12:
Fz + E(Ix); (F))) J n=1 + E(HL
13: Define (+)) [tN/T], set Da F1)
14: while not converged do
15:
for 1 = 1...
16:
20 F(((())
17:
~ - Sel)
18:
Perform gradient descent steps on Vo ||e 1e---c F2)]
19:
end for
20:
1 T
21:
Predict Fo F(1
22:
fr-1 VI-5. (ft Bc50) + o,b
23:
end for
24:
IN = D(ta)
25: Perform gradient descent steps Vo INN D(F
26: Optional: Vo log D. ( V [log Da (Trorna) log Da (Go (Zz)))
27: end while
28:
Return IN
C
Supplementary Experiments and Extended Results
Visual Comparison: As demonstrated in the supplemental visual comparisons (Fig. 8 10 LASQ exhibits remarkable stability across both controlled laboratory settings and challenging real-world see- narios. Figures FiguresBaanDDssstemmaaiie systematically visualize LASQ's consistency in ground truth-annotated scenes, where it maintains precise alignment with reference images in terms of color temperature, dynamic range distribution, and fine-gun tine-grained texture preservation. The visual trajectories across multiple test cases confirm that LASQ effectively decouples scene semantics from lighting interference, avoiding the common pitfalls of oversaturation detail loss observed in supervised baselines. Fig. 10 further underscores LASQ's robustness under extreme conditions, specifically in nocturnal environments and localized overexposure scenarios settings that are notoriously difficult for conventional methods. In night-time scenes with extremely low ambient illumination, LASQ demonstrates pronounced ability to preserve structural integrity and suppress noise amplification, resulting outputs that maintain visual coherence and readability. Unlike comparative methods that often introduce flare artifacts, chromatic aberrations, or abrupt luminance shifts under high-contrast lighting, LASQ adaptively regulates brightness and minimizes highlight clipping, ensuring that important spatial detail is lost. Particularly in overexposed regions-suin as headlights, reflective surfaces, or artificially illuminated zones zone retains nuanced intensity gradients and avoids flattening unnatural glow, thereby delivering smooth tonal transitions that remain faithful to human perceptual expectations. These results confirm that LASQ not only generalizes well across diverse lighting conditions but also excels where existing techniques tend to break down.
To provide more comprehensive evaluation of LASQ and LASQ++ under realistic, unconstrained conditions, we present in Table quantitative comparison on five widely used no-reference low-light image enhancement benchmarks: DICM, NPE, VV, LIME, and MEF,
Quantitative Results:

---

Iapat
(b) EakahieaCAN
KauD
(4) Ligh-ail.flskk
(e) NeRCu
Ub PuelZ
(
Figure 8: The supplemental qualitative comparison of our method and competitive methods on the LOLv1 test sets. "LASS++* denotes the incorporation of unpaired normal-light references.
O
O
Jupet
to EstatteaaA
RaD
LighecEttbarce
(c) PetCs
10
0:
Dt
Figure 9: The supplemental qualitative comparison of our method and competitive methods on the LSRW test sets. "LASQ++" denotes the incorporation of unpaired normal-ligh light references.
These datasets lack paired ground-truth (GT) normal-light references, making them a robust testbed for assessing generalization and real-world applicability. We evaluate performance using two standard no-reference metries-NIQE and PI-and compare LASQ and LASQ++ against broad range of sfte-off-thear supervised (SL) and unsupervised (UL) methods. As shown, LASQ consistently achieves the best or second-best results across almost all datasets and metries, outperforming all other unsupervised methods and remaining competitive with even several fully supervised approaches. This highlights the strong generalization capability of LASQ, which requires no paired data and yet adapts effectively to diverse lighting conditions. LASQ++, which incorporates unpaired normal- light references during training, pushes perceptual quality even further. While this slightly reduces robustness in the most challenging cross-domain settings, it reinforces the complementary strengths of our two models: LASSQ ideal for broad deployment due its high robustness and adaptability, whereas LASQ++ excels when enhanced fidelity is needed in target-speccfic domains.
25

---

( Japs
Bu ExiglaenAAN
5c KmD
6c. SefO
G1 Pazli
No Ground Thath Bad haup
No Grounel Wrath Hen Imup
LIE
Figure 10: Visual method comparison in challenging scenarios. "LASQ++" denotes the incorporation of unpaired normal-light references.
D Computation Resources and Hyper-parameter Tuning
All experiments were conducted on high- performacee computing node equipped with four NVIDIA A100 80GB GPUs interconnected via NVLink. The system specifications are as follows:
OS: Ubuntu 22.04 LTS with Linux 5.15 kernel
CPU: Dual AMD EPYC 7763 64-Core @ 2.45GHz (128 cores/256 threads)
GPU Interconnect: NVLink 3.0 (600GB/s bisectional bandwidth)
Memory: 1TB DDR4 ECC @ 3200MHz
Storage: 16TB NVMc SSD RAID (3.5GB/s sustained read)
Accelerators: 4xNVIDIA A100 80GB (FP32: 19.5 TELOPS, FP16: 312 TFLOPS)
The hyperparameter configuration for the proposed framework is comprehensively summarized as follows. All experiments are conducted four NVIDIA A800 GPUs utilizing Python 3.9 and PyTorch 2.0 with a fixed batch size of 16, employing the Adam optimizer with a denoising diffusion learning rate of 2 x 10-5 and sampling ratio & = 3. The loss weighting coefficients Aa- Ag. and AGAN are empirically set to 0.9, 0.005, and 0.7 respectively when adversarial training is activated. The diffusion process operates over T=1000 time steps with U-Net-based noise estimation architecture. In the luminance adaptation framework, the power-law adjustment parameters a, initialized to 2, 0.1, and 0.01 respectively, while the
and 5 governing local contrast enhancement MCMC sampling process employs an adaptive step size X = 0.2 to balance exploration-explloiaaton dynamics across hierarchy levels. The temporal mapping function (t)) synchronizes N 100 hierarchical guidance levels with the T'step diffusion through linear interpolation. The truncated Gaussian distribution for LAO sampling bounded by 7min and Ymax derived dynamically from image statistics, ensuring physically plausible luminance adjustments. For adversarial training augmentation, the diseriminator De maintains architectural hyperparameters aligned with LSGAN conventions (including convolutional layer configurations and spectral normalization usage), though implemented with standard binary cross-entropy objectives rather than least-squares formulations. The network depth and feature channel dimensions are adaptively scaled according to the spatial resolution of input pairs from the generator's 's decomposition process, while preserving LSGAN's fundamental design principles in layer progression and discriminator receptive field structure.
26

---

Table 5: Supplementary quantitative comparisons on the no-reference image datasets from the main text, with the best-perfior best-performing results marked in red and the second-bess blue. The notations "SL." and "UL" respectively represent supervised and unsupervised learning approaches. "LASQ++" denotes the incorporation of unpaired normal normal-light references.
Type
Mathod
DICM
NPE
VV
LIME
MEF
NIQEL
PtJ
NIQEL
PL
NIQEL
P1l
NIQEU
PL
NIQEL
PL
3.242
4.732
3.727
4.802
KinD=+
4.027
3.999
4.005
3.144
3.586
2.773
4.035
3.217
3.874
3.285
LCDPNet
4.110
3.250
4.126
3.127
5.039
3.3/17
1.128
3.332
3.912
3.398
URetinexNet
4.774
3.563
4.028
3.153
3.851
2.891
3.987
3.104
3.721
3.185
SMG
6.224
4.228
5,300
3.627
5.752
3.737
5.312
3.615
5.028
3.804
PyDiff
4.499
3.792
4.082
3.268
4.360
3.678
4.412
3.685
4.228
3.572
Zero-DCE
3.951
3.149
3. 3.826
2.918
5,080
3.307
3.625
3.512
3.217
EnlightenGAN
3.832
3.256
3.775
2.953
3.689
2.749
3.427
3.424
3.524
3.108
SC1
4.519
3.700
4.124
3.534
5.312
3.648
4.0332
3.518
3. 898
3.415
PairLIEE
4.282
3.469
4.661
3.543
3.373
2.734
3.782
3.215
3.412
3.028
UL
SCL-LLE
5.129
3.809
4.873
3.692
5.513
4.316
5.104
4.302
4.872
4.115
3.902
3.037
3.765
LightenDiffusion
3.724
3.144
3.618
2.879
2.941
2.558
3.218
3. 128
3.305
3.024
LASQ
3.713
3.128
3.571
2.764
2.777
2.623
3.152
3.002
3.294
3.001
LASQ++
3.723
3.137
3.601
2.789
2.850
2.691
3.167
3.046
3.309
3.013
Table Supplementary ablation results.
Method
Pre-trained Dataset
VV
MEF
NPE
DICM
LIME
LOLvI LSRW MEF NIQEL
PL]
NIQEL
P11
NIQE,
NIQEL
PI1
NIQE,
2.777
2.623
3.291
3.001
3.571
2.761
3.715
3.128
3.152
3.002
LASQ
1
2.801
2.637
3.307
3.019
3.584
2.762
3.750
3.160
3.191
3.222
2.882
2.703
3.287
3.010
3.627
2.758
3.744
3.177
3.248
3.335
E
Ablation
As shown in Table 6. we present extend ablation studies systematically evaluate the proposed framework. LASQ is trained on three datasets (LOLv1, LSWR, and MEF with manually curated training splits augmented via random cropping), followed by cross-dataset evaluations on MEF and VV benchmarks. Empirical results demonstrate LASQ's superior generalization capability in unseen scenarios, achieving consistent reconstruction quality under diverse illumination conditions. This evidence confirms that LASQ avoids overfitting to region-specific normal-light patterns and exhibits reduced sensitivity to pretraining data selection. Two principal advantages are emphasized: (I) Cross-domain adaptation through layered luminance modeling enables high-fidelity visual outputs without paired supervision; (2) Effective suppression of noise and artifacts under extreme low-light conditions, substantially improving perceptual quality. These advancements highlight LASQ's potential to redefine low-light image enhancement paradigms by harmonizing physical priors with Kata-driven learning.
F
Limitations
The main limitations of our approach stem from its hierarchical sampling depth N and diffusion step count T: N grows, the number of local correction patches doubles at each level, and T increases, each diffusion step must incorporate the corresponding hierarchical features, jointly large N and T lead exponential increases in computation and memory demands. Moreover, the final enhancement quality is quite sensitive to the choice of N (too few layers underfits, too many layers overfits and amplifies noise) and (insufficient steps yield coarse results, excessive steps waste resources), well as to key hyperparameters in the 7-calculation (e.g. the contrast gain terms y and 8 which must be manually tuned balance enhancement strength against artifact suppression across different scenes.
27

---

G
Impacts
Our LASQ advances low-light imaging for critical applications like target detection, self-driving and medical diagnosties, where reliable scene interpretation under poor illumination is essential. Our LASQ mimics natural light adaptation improve nighttime safety. Its unsupervised design removes the need for paired data, expanding access to low-light enhancement for applications lacking annotated datasets.
28


Reviews:
[{"review_id": "vqrF7Upgqn", "reviewer": "Reviewer_YCBh", "summary": "In this paper, authors propose LASQ to tackles the challenge of low-light image enhancement without requiring paired data. LASQ achieve this goal by reframing luminance adjustment as a multi-scale and physics‐informed statistical process. In practice, it leverages empirical observations of natural image intensity transitions to generate multi‐scale luminance operators for coarse‐to‐fine block‐wise refinement (within diffusion model). The result is a zero‐reference enhancement method that achieves sota performance on most of paired and unpaired benchmarks.", "questions": "- What is the computational cost of the proposed method? It requires block-wise refinement and multi-scale optimization from coarse to fine, which I assume is expensive, yet neither the main paper nor the supplementary material report any runtime or resource-usage measurements.\n\n- I also wonder how robust the method is when the power-law intensity assumption breaks down—for example, in scenes lit by a strong spotlight or with intense background light. Can it still perform well under such extreme conditions? Since it relies on manually designed statistical priors, it may only model the mapping from typical natural low-light to normal-light conditions and struggle with more complex or atypical cases.\n\n- The method depends heavily on empirically chosen hyperparameters (e.g., α, β, λ). How sensitive is its performance to these values? Has its stability been evaluated under different hyperparameter settings?\n\n- The paper claims an improvement in color fidelity. Have you validated this claim using specialized color-difference metrics to provide quantitative evidence?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths\n1. the coarse-to-fine, block-wise refinement preserves both global consistency and local details at the same time. the multi-scale, power-law–based framework aligns with the physical process of illumination change is interesting and more explainable.\n2. the proposed method can work without the presence of normal-light references, which is more useful in real-world settings\n3. LASQ distills the authors’ observation of pixel intensity transitions in natural images ( expressed via the Luminance Variation Coordinate and power-law curves) into an intersting sampling operators. This elegant fusion of physical-statistical modeling with generative diffusion guidance is novel strategy or tasteful philosiphy to develope diffusion model for low-light enhancement.\n\n-Weakness:\nPlease see questions for details.\n1. The report of required computational cost is missing\n2. the pipeline training's sensity on those manual chosen hyperparameters is unknown.", "was_revised": true, "final_justification": "Overall, all of my concerns are well addressed with thorough experiments and explanations.\nI maintain my original score, as I still lean toward a weak accept.", "rebuttal": "We sincerely thank you for your valuable feedback and constructive suggestions. We are particularly encouraged by your recognition of LASQ as **\"elegant fusion\"**, **\"novel strategy\"** and **\"tasteful philosophy\"**. Below, we provide a point-by-point response to your comments.\n\n**Q1 & W1: Computational Cost**\n\nWe have now added detailed computational complexity metrics below (NVIDIA A800, LOL dataset). We clarify that the coarse-to-fine MCMC sampling mechanism is only used during training and is embedded into the forward diffusion process. It guides the model to traverse luminance layers in a hierarchical manner, enabling structured learning of light propagation. During inference, our model only performs the denoising step conditioned on the low-light input within the diffusion model, which is significantly more efficient. \n\nWe compare LASQ against both early non-diffusion-based methods (e.g., EnlightenGAN, KinD++), and recent diffusion-based approaches (e.g., WCDM, LightenDiffusion). While the early methods are lightweight, their performance lags far behind diffusion-based models across all key metrics. Existing diffusion models, although significantly more effective, tend to suffer from high computational cost due to deep architectures and iterative sampling. LASQ, while maintaining the performance advantages of diffusion models, achieves inference efficiency comparable to non-diffusion-based methods. This makes it highly suitable for real-world deployment. Considering the substantial performance gain without reliance on reference images, the moderate computational overhead of LASQ is a practical and acceptable trade-off. In future work, we also plan to explore lightweight variants of our framework for further efficiency.\n\n|      Method      | FLOPs (G) | Params (M) | Inference Time (ms) | Memory Usage (MB) | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| :--------------: | :-------: | :--------: | :-----------------: | :---------------: | :-------: | :------: | :------: |\n|   EnlightenGAN (2019)   |   16.45   |    8.64    |        170.16       |       241.48      |   17.606   |   0.653   |   0.319   |\n|      KinD++ (2021)   |   17.49   |    8.27    |       4279.70       |       372.19      |   17.752   |   0.758   |   0.198   |\n|       NeRCo (2023)    |   184.20  |    23.30   |        354.77       |      2320.87      |   19.738   |   0.740   |   0.239   |\n|      PairLIE (2023)    |   81.84   |    8.34    |        190.70       |      3499.79      |   19.514   |   0.731   |   0.254   |\n|       WCDM (2023)      |   374.47  |    22.92   |        206.66       |      6017.86      |   20.105   |   0.795   |   0.211   |\n| LightenDiffusion (2024) |   367.99  |    27.83   |        257.94       |      8049.95      |   20.453   |   0.803   |   0.192   |\n|     LASQ (ours)    |   219.75  |    24.08   |        213.89       |      6496.68      | **20.481** | **0.814** | **0.191** |\n\n**Q2: Robustness to Extreme Lighting**\n\nWe would like to clarify that our approach employs a coarse-to-fine MCMC sampling strategy to fit the luminance adaptation space, rather than rigidly enforcing the power-law assumption. As discussed in Appendix A (paragraph 4), we reinterpret the strict power-law model as a relaxed prior and leverage MCMC sampling—integrated with the forward process of a diffusion model—to explore the distribution space.\n\nThis design enables LASQ to flexibly capture a wider family of luminance mappings that are only loosely guided by the physics-inspired prior, rather than being constrained to a fixed analytical form. As a result, our method does not critically depend on the power-law assumption itself. The MCMC-based inference operates at a structured level, incorporating spatial context, which allows it to remain robust under spatially inconsistent or extreme lighting conditions—well beyond the capabilities of per-pixel or strictly model-driven approaches.\n\nTo validate the robustness and stability of LASQ across diverse lighting conditions, we evaluate it on two additional datasets: **DICM**, featuring scenes with “intense background light”, and **Light-Effects**, containing “strong spotlights”. In the tables below, each metric is presented as DICM / Light-Effects. As shown, LASQ consistently achieves the best performance across all metrics on both datasets, demonstrating its strong effectiveness even under conditions that significantly deviate from the power-law assumption.\n\n| Method           | NIQE ↓         | PI  ↓ |\n|------------------|----------------|----------------------|\n| EnlightenGAN     | 2.7583 / 3.5451 | 2.4137 / 3.3061      |\n| NeRCo            | 2.7690 / 3.5462 | 2.5701 /   3.2365     |\n| KinD\\_plus       | 2.8584 / 3.6449 | 2.6532 / 3.3233      |\n| LightenDiffusion | 2.6889 / 3.5503 | 2.9789 / 3.6606      |\n| PairLIE          | 3.2412 / 3.8477 | 3.6262 / 3.9066      |\n| SCI              | 3.1745 / 3.9607 | 2.5552 / 3.2683      |\n| SCL-LLE          | 2.6584 / 3.7367 | 2.4543 / 4.2245  |\n| URetinex-Net     | 3.0365 / 3.6923 | 2.9806 / 3.7060      |\n| **LASQ**  | **2.6190 / 3.2479** | **2.3633** / **3.1020**  |\n\nIn addition to the datasets presented in the main paper and supplementary material (**LOL**, **LSRW**, **NPE**, **VV**, **LIME**, **MEF**), we also tested LASQ on several challenging unseen domains—**LOL_blur**, **LED**, and **ELD**. Due to space constraints, detailed results are provided in the responses to other reviewers. These experiments further demonstrate the strong generalization ability and robustness of LASQ across diverse and atypical low-light scenarios.\n\n**Q3 & W2: Hyperparameter Sensitivity**\n\nTo address concerns about empirical parameter selection, we provide a hyperparameter sensitivity analysis on the **LSRW** dataset.\n\n|      Param     |           Value           |               PSNR ↑              |              LPIPS ↓              |               SSIM ↑              |\n| :------------: | :-----------------------: | :-------------------------------: | :-------------------------------: | :-------------------------------: |\n|   \\$\\alpha\\$   |  0.05 / 0.15 / 0.3 / 0.6  | 17.81 / **18.10** / 17.92 / 17.84 | **0.319** / 0.322 / 0.320 / 0.324 | 0.512 / **0.543** / 0.530 / 0.519 |\n|    \\$\\eta\\$    |   0.1 / 1.0 / 3.0 / 6.0  | 17.85 / **18.35** / 18.17 / 17.95 | 0.335 / **0.321** / 0.324 / 0.329 | 0.537 / 0.543 / **0.546** / 0.540 |\n| \\$\\lambda\\_d\\$ |  0.1 / 1.0 / 10 / 20 | 17.82 / **18.04** / 17.85 / 17.87 | 0.324 / **0.315** / 0.318 / 0.323 | 0.545 / **0.553** / 0.549 / 0.531 |\n| \\$\\lambda\\_g\\$ | 0.001 / 0.005 / 0.01 / 0.1 | 17.76 / **18.22** / 18.16 / 17.88 | 0.312 / 0.310 / **0.309** / 0.311 | 0.536 / **0.548** / 0.547 / 0.540 |\n\n\nAs illustrated in the table, we systematically varied key hyperparameters including $\\alpha$, $\\eta$, $\\lambda_d$, and $\\lambda_g$ over a range of values ($\\beta_{p}$ is determined by $\\eta$). The results show that while performance slightly fluctuates with different settings, the overall impact on metrics remains moderate. For instance, varying $\\alpha$ between 0.05 and 0.6 only causes a minor PSNR change (within 0.3 dB) and negligible shifts in perceptual scores. Similarly, other hyperparameters demonstrate a stable trend without sharp degradation. These experiments demonstrate that our method is not overly sensitive to hyperparameter selection, and maintains consistently strong performance across a broad range of settings—highlighting its robustness, practical stability, and generalization potential.\n\n**Q4: Color Fidelity Evaluation**\n\nTo validate our claim of improved color reproduction, we performed a comprehensive quantitative evaluation on the **LOL** dataset using three widely recognized metrics that assess perceptual color accuracy: CIE76 (ΔE\\*ab), CIEDE2000 (ΔE₀₀), and FSIMc. ΔE\\*ab and ΔE₀₀ measure perceptual differences in hue, luminance, and chrom, while FSIMc captures perceptual image quality by incorporating color similarity.\n\n| Method           | ΔE\\*ab ↓   | ΔE₀₀ ↓    | FSIMc ↑    |\n| ---------------- | ---------- | --------- | ---------- |\n| EnlightenGAN     | 35.734     | 17.915    | 0.8792     |\n| KinD++           | 30.950     | 14.740    | 0.9243     |\n| LightenDiffusion | 32.388     | 15.631    | 0.9178     |\n| NeRCo            | 26.682     | 12.607    | 0.9342     |\n| PairLIE          | 30.790     | 14.298    | 0.9192     |\n| SCI              | 92.862     | 58.559    | 0.4585     |\n| SCL-LLE          | 66.855     | 37.133    | 0.6928     |\n| URetinex         | 26.080     | 12.551    | 0.9310     |\n| **LASQ**       | **19.649** | **9.251** | **0.9567** |\n\nAs shown, our proposed LASQ achieves the lowest color difference scores and the highest FSIMc, confirming its superior color fidelity. These results quantitatively support our claim that LASQ delivers more faithful and perceptually accurate color restoration compared to other methods.\n\n**All supplementary tables and visualizations will be included in the final version of the paper or appendix.**"}, {"review_id": "5rfDyLTfi1", "reviewer": "Reviewer_dpMf", "summary": "This paper introduces a novel framework for low-light image enhancement (LLIE) called Luminance-Aware Statistical Quantization (LASQ). The approach redefines LLIE by addressing the inherent challenges of low-light image enhancement in practical settings, focusing on realistic and continuous luminance transitions rather than pixel-level mappings. It leverages hierarchical power-law distributions to model luminance transitions and proposes a statistical sampling process to emulate these transitions, allowing for better generalization and adaptability across various lighting conditions. The framework employs a diffusion model for unsupervised learning and achieves state-of-the-art performance, especially in scenarios where normal-light references are unavailable. The authors also provide extensive experiments demonstrating the effectiveness of LASQ in both reference-based and reference-free settings.", "questions": "1. Can you provide more details on the selection and tuning of key hyperparameters (e.g., α and β_P, etc.)? In particular, how sensitive are the models to changes in these parameters, and how can these parameters be optimized for a specific dataset?\n\n2. Given the multi-level sampling structure, how do you ensure that the MCMC process converges efficiently without excessive iterations?\n\n3. The paper mentions that power-law transformations are problematic in low-intensity areas, where small intensity variations cause large shifts in the model. How do you handle the instability in low-intensity areas?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. It introduces a Luminance Variation coordinate system and a power-law adaptation operator, grounding low-light to normal-light mapping in a physically motivated statistical model rather than purely data-driven method.\n\n2. The proposed method allows for unsupervised learning without the need for paired datasets, a significant advantage for real-world applications where paired data may not be available.\n\n3. It achieves superior NIQE and PI scores on unpaired datasets (e.g., DICM, NPE, VV) compared to other unsupervised approaches, demonstrating the method’s adaptability and robustness across diverse scenes.\n\n\nWeaknesses:\n1. The paper mentions that power-law transformations are problematic in low-intensity areas, where small intensity variations cause large shifts in the model. How to handle the instability in low-intensity areas?\n\n2. The multi-scale MCMC sampling requires numerous iterations per image and multiple power-law operators, resulting in substantial computational and memory overhead during inference.\n\n3. The performance of the method is highly dependent on the choice of hyperparameters, such as the power-law exponents, sampling ratio, and the number of iterations in MCMC. The method would benefit from a clearer set of guidelines or automatic tuning mechanisms to avoid overfitting to specific datasets or scenarios.", "was_revised": true, "final_justification": "The authors basically resolved my concerns, especially the fact experimental parameters discussions, which sounds more convincing. Since the initial value is 4, I hence keep that as the final rating.", "rebuttal": "We sincerely thank the reviewer for identifying several important concerns and for the opportunity to further clarify our method. We address each point in detail below.\n\n**Q1 & W3: Hyperparameter Selection and Sensitivity**\n\nWe conducted a hyperparameter sensitivity analysis on the **LSRW** dataset :\n\n|Param| Value| PSNR ↑ |LPIPS ↓| SSIM ↑ |\n| :------------: | :-----------------------: | :-------------------------------: | :-------------------------------: | :-------------------------------: |\n|   \\$\\alpha\\$   |  0.05 / 0.15 / 0.3 / 0.6  | 17.81 / **18.10** / 17.92 / 17.84 | **0.319** / 0.322 / 0.320 / 0.324 | 0.512 / **0.543** / 0.530 / 0.519 |\n|    \\$\\eta\\$    |   0.1 / 1.0 / 3.0 / 6.0  | 17.85 / **18.35** / 18.17 / 17.95 | 0.335 / **0.321** / 0.324 / 0.329 | 0.537 / 0.543 / **0.546** / 0.540 |\n| \\$\\lambda\\_d\\$ |  0.1 / 1.0 / 10 / 20 | 17.82 / **18.04** / 17.85 / 17.87 | 0.324 / **0.315** / 0.318 / 0.323 | 0.545 / **0.553** / 0.549 / 0.531 |\n| \\$\\lambda\\_g\\$ | 0.001 / 0.005 / 0.01 / 0.1 | 17.76 / **18.22** / 18.16 / 17.88 | 0.312 / 0.310 / **0.309** / 0.311 | 0.536 / **0.548** / 0.547 / 0.540 |\n\nWe varied key hyperparameters ($\\beta_p$ is derived from $\\eta$) over a range. Results show only moderate metric changes. For example, $\\alpha$ from 0.05 to 0.6 alters PSNR by <0.3 dB, with minimal perceptual impact. Other hyperparameters show similar stability, indicating strong robustness to tuning. We also evaluated two structural parameters on both **LSRW** and **LMIE**:\n\n| Method | PSNR ↑ | SSIM ↑ | LPIPS ↓ | NIQE ↓ | PI ↓  |\n|------------|--------|--------|---------|--------|--------|\n| $k = 1$ | 16.23  | 0.481  | 0.341   | 4.31   | 4.19 |\n| $k = 2$ | 17.38  | 0.517  | 0.392   | 3.88   | 3.48 |\n| $k = 3$ | 18.12  | 0.545  | 0.297   | 3.11   | 2.96  |\n| $k = 4$ | 18.24  | 0.551  | 0.293   | 3.14   | 3.02|\n| $N = 90$   | 16.21  | 0.480  | 0.391   | 4.29   | 4.21 |\n| $N = 100$  | 18.10  | 0.542  | 0.296   | 3.16   | 3.02 |\n| $N = 110$  | 18.26  | 0.546  | 0.300   | 3.13   | 2.99 |\n| $N = 120$  | 18.17  | 0.549  | 0.289   | 3.08   | 2.93 |\n\nPerformance improves with larger $k$ and $N$, but saturates beyond $k{=}3$ and $N{=}100$; we adopt these as defaults for a quality-efficiency trade-off. Results show our method is robust and stable across a wide hyperparameter range, indicating strong generalization.\n\n\nIn addition to the datasets reported in the main text (**LOL**, **LSRW**, **DICM**, **NPE**, **VV**, **LIME**, **MEF**), we tested LASQ on several challenging unseen domains—**LOL_blur**, **LED**, **ELD**, and **Light-Effects**—using the same fixed hyperparameters. Due to space constraints, detailed results are shown in the responses to the other reviewers. Consistently strong performance in these scenarios further confirms that LASQ generalizes well across diverse domains without retraining or re-tuning.\n\nTo improve generalization to new datasets, we add a hyperparameter summary table in the appendix. While tuning often generalizes well, we further explore an **hidden Markov model (HMM)-based** auto-tuning framework that updates both network and hyperparameters per batch. Specifically, the emission probability is:\n\n$p(\\mathcal{F}^{t}\\_{H}|{\\Theta}^{t}) = p(\\mathcal{F}^{t}\\_{H}|{\\theta}^{t}\\_\\text{diff},\\mathcal{I}\\_{H}^t)p(\\mathcal{I}\\_{H}^t|\\gamma)p(\\gamma|\\theta^{t}\\_\\text{hyper})$\n\nwhere hidden states $\\Theta^t=\\\\{{\\theta}^{t}\\_\\text{diff},\\theta^{t}\\_\\text{hyper}\\\\}$. $\\gamma$ is drawn from a hyperparameter-dependent distribution. $p(\\mathcal{I}_H^t|\\gamma)$ models MCMC sampling, and $p(\\mathcal{F}_H^t|\\cdot)$ corresponds to the diffusion process. The state transition follows:\n\n$p({\\Theta}^{t+1}|{\\Theta}^{t},\\mathcal{F}^{t}\\_{H}) = p({\\Theta}^{t+1}|{\\Theta}^{t},\\mathcal{L}\\_{\\text{total}}^{t})p(\\mathcal{L}\\_{\\text{total}}^{t}|\\mathcal{F}^{t}\\_{H})$\n\nThis captures Bayesian updates of parameters. For gradient-based hyperparameter updates, we apply the chain rule:\n\n$\\frac{\\partial \\mathcal{L}\\_{\\text{total}}^t}{\\partial \\theta^t\\_{\\text{hyper}}}\n= \\mathbb{E}\\_{\\gamma \\sim p(\\gamma \\mid \\theta^t\\_{\\text{hyper}})} \\left[\n    \\frac{\\partial \\mathcal{L}\\_{\\text{total}}^t}{\\partial \\mathcal{F}\\_H^t} \\cdot\n    \\frac{\\partial \\mathcal{F}\\_H^t}{\\partial \\gamma} \\cdot\n    \\frac{\\partial \\gamma}{\\partial \\theta^t\\_{\\text{hyper}}}\n\\right]$\n\nThis framework reduces manual tuning and mitigates overfitting by dynamically adapting to data distributions. Preliminary experiments on the LSRW dataset show about 6% performance improvement.\n\n**Q2 & W2: Clarification on MCMC Sampling Cost and Convergence**\n\nWe have now added detailed computational complexity metrics below (NVIDIA A800, LOL dataset). We clarify that the coarse-to-fine MCMC sampling mechanism is only used during training and is embedded into the forward diffusion process. It guides the model to traverse luminance layers in a hierarchical manner, enabling structured learning of light propagation. During inference, our model only performs the denoising step conditioned on the low-light input within the diffusion model, which is significantly more efficient. \n\nWe compare LASQ against both early non-diffusion-based methods (e.g., EnlightenGAN, KinD++), and recent diffusion-based approaches (e.g., WCDM, LightenDiffusion). While the early methods are lightweight, their performance lags far behind diffusion-based models across all key metrics. Existing diffusion models, although significantly more effective, tend to suffer from high computational cost due to deep architectures and iterative sampling. LASQ, while maintaining the performance advantages of diffusion models, achieves inference efficiency comparable to non-diffusion-based methods. This makes it highly suitable for real-world deployment. Considering the substantial performance gain without reliance on reference images, the moderate computational overhead of LASQ is a practical and acceptable trade-off. In future work, we also plan to explore lightweight variants of our framework for further efficiency.\n\n|      Method      | FLOPs (G) | Params (M) | Inference Time (ms) | Memory Usage (MB) | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| :--------------: | :-------: | :--------: | :-----------------: | :---------------: | :-------: | :------: | :------: |\n|   EnlightenGAN (2019)   | 16.45 |    8.64    |        170.16       |       241.48      |   17.606   |   0.653   |   0.319   |\n|    KinD++ (2021)| 17.49 |  8.27  |   4279.70  |       372.19      |   17.752   |   0.758   |   0.198   |\n|    NeRCo (2023)  | 184.20 | 23.30 |        354.77       |      2320.87      |   19.738   |   0.740   |   0.239   |\n|   PairLIE (2023) | 81.84   |  8.34 |   190.70     |      3499.79      |   19.514   |   0.731   |   0.254   |\n|    WCDM (2023)   | 374.47| 22.92 |        206.66       |      6017.86      |   20.105   |   0.795   |   0.211   |\n| LightenDiffusion (2024) | 367.99  |    27.83   |        257.94       |      8049.95      |   20.453   |   0.803   |   0.192   |\n|   LASQ (ours) |   219.75  |  24.08   |        213.89       |      6496.68      | **20.481** | **0.814** | **0.191** |\n\nWhile we reiterate that MCMC sampling is only applied during training, we fully acknowledge the importance of convergence efficiency and training stability. Notably, the sampling process is embedded into the forward diffusion trajectory, inherently generating a large number of diverse samples throughout training. This ensures thorough exploration of the latent space. Furthermore, the structured design of the latent operator space, the coarse-to-fine hierarchical sampling strategy, and the learned Markov regularization collectively facilitate natural and stable convergence without the need for excessive iterations.\n\n**Q3 & W1: Clarification on \"Instability in Low-Intensity Areas\"**\n\nWe would like to clarify that our approach employs a coarse-to-fine MCMC sampling strategy to fit the luminance adaptation space, rather than rigidly enforcing the power-law assumption. As discussed in Appendix A (paragraph 4), we reinterpret the strict power-law model as a relaxed prior and leverage MCMC sampling—integrated with the forward process of a diffusion model—to explore the distribution space.\n\nThis design enables LASQ to flexibly capture a wider family of luminance mappings that are only loosely guided by the physics-inspired prior, rather than being constrained to a fixed analytical form. As a result, our method does not critically depend on the power-law assumption itself. The MCMC-based inference operates at a structured level, incorporating spatial context, which allows it to remain robust under spatially inconsistent or extreme lighting conditions—well beyond the capabilities of per-pixel or strictly model-driven approaches.\n\nTo further support this claim, we conduct comparative evaluations on the **ELD** dataset, which contains extremely low-light scenarios with strong noise and minimal luminance signal. As shown below, LASQ achieves competitive performance compared to prior methods, demonstrating its robustness even under severe low-intensity degradations. Its strong performance in additional challenging conditions—such as motion blur, extreme dynamic range, and LED flicker—is presented in our responses to other reviewers, further highlighting the stability and generalizability of LASQ.\n\n| Method| PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| - | - | -| - |\n| EnlightenGAN| 16.78  | 0.485  | 0.539 |\n| KinD++ | 12.48  | 0.310  | 0.888|\n| LightenDiffusion | 13.66  | 0.364  | 0.826 |\n| NeRCo | 10.29  | 0.260  | 0.714  |\n| PairLIE | 17.45  | 0.485  | 0.524  |\n| SCI    | 14.53  | 0.149  | 0.568   |\n| SCL-LLE | 14.66  | 0.158  | 0.547 |\n| URetinex-Net | 16.73  | 0.463  | 0.544 |\n| **LASQ**  | **17.69**  | **0.610**  | **0.409**  |\n\n**All supplementary tables and visualizations will be included in the final version of the paper or appendix.**"}, {"review_id": "GA9hF8krqm", "reviewer": "Reviewer_r7sk", "summary": "The paper proposes a novel, physics-inspired framework for low-light image enhancement (LLIE). \nThe key innovation lies in reformulating LLIE as probabilistic sampling over hierarchical luminance layers that follow power-law distributions.\nInstead of using fixed mappings or empirical curves, LASQ applies a Markov Chain Monte Carlo (MCMC) strategy to sample luminance adaptation operators at different granularities, ranging from global adjustments to local refinements. \nExperiments demonstrate that LASQ attains state-of-the-art performance on non-reference datasets and comparable results to reference-based methods on paired datasets.", "questions": "1. The hierarchical power-law distributions mentioned in the paper follow a coarse-to-fine design, with features from all levels being fused through weighted averaging within the diffusion model. \nHowever, the weights used in this process are fixed and not learnable.\n It would be worth exploring whether these weights could be made learnable, allowing the model to adaptively assign importance to different hierarchical levels based on the data, potentially improving fusion effectiveness and overall performance.\n2. LASQ employs Hierarchical Luminance Modeling to process low-light images at both local and global levels, followed by the use of a diffusion model framework to fuse images across different layers.\n However, it remains unclear whether the use of a diffusion model is strictly necessary for this fusion step. \nThe authors should clarify why a diffusion-based approach is chosen over potentially more lightweight alternatives, and whether similar results could be achieved using more efficient models with lower computational cost.\n3. The paper adopts a diffusion model along with a hierarchical strategy, which increases the overall algorithmic complexity. \nTherefore, a comparison of computational complexity,such as inference time, number of parameters, and memory consumption, between the proposed method and existing approaches should be included.\n4. In the first row of Figure 3, the image generated by the LASQ method appears overly smooth, lacking fine-grained texture. In comparison, the results produced by NeRCo and PairLIE demonstrate better preservation of image details.", "limitations": "Please see the weakness and question parts.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Theoretical Soundness: The paper presents a well-founded framework based on power-law distributed luminance statistics, supported by empirical observations\n2. Comprehensive Experiments: The paper includes detailed evaluations on both paired and unpaired benchmarks, using a wide array of perceptual and fidelity-based metrics \n3. Well-structured Paper: The paper follows a logical structure (motivation, method, experiments), and includes detailed tables and figures comparing against a large number of baselines.\n\nWeaknesses:\n1. Computational Complexity: While the diffusion process and hierarchical sampling improve performance, they are computationally expensive and may limit real-time applications or deployment on low-power devices.\n2. Hyperparameter Sensitivity: The method introduces several hyperparameters (e.g., MCMC sampling step ) whose selection may impact robustness.", "was_revised": true, "final_justification": "I have carefully read the authors’ response. Overall, I am relatively satisfied, especially with the extended explanation and comparisons on model efficiency. I believe my initial positive rating has already well reflected the quality of the paper.", "rebuttal": "We sincerely thank you for your valuable feedback and constructive suggestions. We are particularly encouraged by your recognition of LASQ as **\"a novel, physics-inspired framework\"**. Below, we provide a point-by-point response to your comments.\n\n**Q1: Learnable Fusion Weights via HMM-Based Adaptation**\n\nThank you for your insightful suggestion — it aligns well with our own research perspective. We further explore an **HMM-based auto-tuning framework** that dynamically updates both the network hyperparameters and the fusion weights on a per-batch basis.\n\nWe begin by randomly initializing the hierarchical weights $W=\\\\{w_1, w_2,...,w_n\\\\}$ for coarse-to-fine feature fusion. At each timestep $t$, the emission probability is defined as:\n\n$p(\\mathcal{F}^{t}\\_{H}|{\\Theta}^{t}) = p(\\mathcal{F}^{t}\\_{H}|{\\theta}^{t}\\_\\text{diff},W,\\mathcal{I}\\_{H}^t)p(\\mathcal{I}\\_{H}^t|\\gamma)p(\\gamma|\\theta^{t}\\_\\text{hyper})$\n\nHere, the hidden states $\\Theta^t$ include diffusion model parameters, hyperparameters and hierarchical fusion weights ($\\Theta^t=\\\\{{\\theta}^{t}\\_\\text{diff},\\theta^{t}\\_\\text{hyper},W\\\\}$), while $\\gamma$ is drawn from a hyperparameter-dependent distribution. The term $p(\\mathcal{I}_H^t|\\gamma)$ models MCMC-based sampling, and $p(\\mathcal{F}_H^t|\\cdot)$ corresponds to the actual diffusion output conditioned on the intermediate latent representation. The state transition follows:\n\n$p({\\Theta}^{t+1}|{\\Theta}^{t},\\mathcal{F}^{t}\\_{H}) = p({\\Theta}^{t+1}|{\\Theta}^{t},\\mathcal{L}\\_{\\text{total}}^{t})p(\\mathcal{L}\\_{\\text{total}}^{t}|\\mathcal{F}^{t}\\_{H})$\n\nThis framework enables the model to **adaptively assign importance to hierarchical levels** based on data distribution, thereby enhancing fusion effectiveness and mitigating risks such as overfitting or suboptimal manual tuning. We will include this auto-tuning strategy as an extension in the revised version to demonstrate its feasibility and performance impact.\n\n**Q2: Necessity and Efficiency of Diffusion-Based Fusion**\n\nIn LASQ, the luminance adaptation space is explored through a progressive MCMC sampling strategy, embedded with the forward process of a diffusion model. Specifically, the sampling over luminance states at different noise levels $t$ is aligned with the trajectory of the diffusion process, enabling structured and gradual adaptation of luminance features. This integration allows for effective exploration of the latent luminance space without requiring heavy supervision or fine-tuning, while ensuring stable convergence across diverse lighting conditions.\n\nThe diffusion model enables unsupervised traversal across hierarchical luminance layers, and provides a principled mechanism to progressively refine luminance representations from coarse global estimates to fine local details. This aligns well with the hierarchical structure of luminance variations in natural scenes, and allows the model to adaptively balance global consistency and local contrast. This layer-wise sampling and fusion strategy enhances the model’s robustness to complex illumination patterns and supports high generalization under diverse low-light conditions, without any reference images.\n\nWe appreciate the suggestion to explore more lightweight alternatives. We will explore adapting LASQ to lightweight models (e.g., CNNs) via a hierarchical luminance-based data augmentation strategy. Specifically, the augmented luminance maps will be allocated to different layers of the CNN, enabling image enhancement through a hierarchical training strategy. This framework maintains the core principle of hierarchical adaptation while significantly reducing computational overhead—all without requiring access to reference images. We consider this a promising direction for future research.\n\n**Q3 & W1: Computational Cost**\n\nWe have now added detailed computational complexity metrics below (NVIDIA A800, LOL dataset). We clarify that the coarse-to-fine MCMC sampling mechanism is only used during training and is embedded into the forward diffusion process. It guides the model to traverse luminance layers in a hierarchical manner, enabling structured learning of light propagation. During inference, our model only performs the denoising step conditioned on the low-light input within the diffusion model, which is significantly more efficient. \n\nWe compare LASQ against both early non-diffusion-based methods (e.g., EnlightenGAN, KinD++), and recent diffusion-based approaches (e.g., WCDM, LightenDiffusion). While the early methods are lightweight, their performance lags far behind diffusion-based models across all key metrics. Existing diffusion models, although significantly more effective, tend to suffer from high computational cost due to deep architectures and iterative sampling. LASQ, while maintaining the performance advantages of diffusion models, achieves inference efficiency comparable to non-diffusion-based methods. Considering the substantial performance gain without reliance on reference images, the moderate computational overhead of LASQ is a practical and acceptable trade-off. Therefore, LASQ strikes a favorable balance between performance and computational efficiency, enabling deployment on low-power devices equipped with less than 8GB of GPU memory (e.g., NVIDIA Jetson AGX Orin), without compromising image enhancement quality. In future work, we also plan to explore lightweight variants of our framework for further efficiency. \n\n|      Method      | FLOPs (G) | Params (M) | Inference Time (ms) | Memory Usage (MB) | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| - | - |- |-|-|-|-|-|\n|   EnlightenGAN (2019)   |   16.45   |    8.64    |        170.16       |       241.48      |   17.606   |   0.653   |   0.319   |\n|      KinD++ (2021)   |   17.49   |    8.27    |       4279.70       |       372.19      |   17.752   |   0.758   |   0.198   |\n|       NeRCo (2023)    |   184.20  |    23.30   |        354.77       |      2320.87      |   19.738   |   0.740   |   0.239   |\n|      PairLIE (2023)    |   81.84   |    8.34    |        190.70       |      3499.79      |   19.514   |   0.731   |   0.254   |\n|       WCDM (2023)      |   374.47  |    22.92   |        206.66       |      6017.86      |   20.105   |   0.795   |   0.211   |\n| LightenDiffusion (2024) |   367.99  |    27.83   |        257.94       |      8049.95      |   20.453   |   0.803   |   0.192   |\n|     LASQ (ours)    |   219.75  |    24.08   |        213.89       |      6496.68      | **20.481** | **0.814** | **0.191** |\n\n**Q4: Texture Preservation in Diffusion-Based Enhancement**\n\nThank you for pointing this issue. As a diffusion-based generative model, LASQ may, in some isolated cases, produce overly smooth results with less detailed texture, particularly under challenging lighting or structure-less regions. However, on the full LSRW test set, LASQ outperforms both NeRCo and PairLIE in terms of all major evaluation metrics, indicating overall superior perceptual quality. Moreover, such texture-smoothing artifacts occur in less than 10% of the test cases.\n\nTo mitigate this issue in future work, we plan to enhance the decoder's detail sensitivity through multi-scale texture refinement modules, and introduce a structure-aware loss function that explicitly penalizes over-smoothing in texture-rich regions.\n\n| Method| PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| - | - | -| - |\n| NeRCo | 17.844  | 0.535  | 0.371  |\n| PairLIE | 17.602  | 0.501  | 0.323  |\n| **LASQ**  | **18.137**  | **0.547**  | **0.308**  |\n\n**W2: Hyperparameter Selection and Sensitivity**\n\nWe conducted a hyperparameter sensitivity analysis on the **LSRW** dataset :\n\n|Param| Value| PSNR ↑ |LPIPS ↓| SSIM ↑ |\n| :------------: | :-----------------------: | :-------------------------------: | :-------------------------------: | :-------------------------------: |\n|   \\$\\alpha\\$   |  0.05 / 0.15 / 0.3 / 0.6  | 17.81 / **18.10** / 17.92 / 17.84 | **0.319** / 0.322 / 0.320 / 0.324 | 0.512 / **0.543** / 0.530 / 0.519 |\n|    \\$\\eta\\$    |   0.1 / 1.0 / 3.0 / 6.0  | 17.85 / **18.35** / 18.17 / 17.95 | 0.335 / **0.321** / 0.324 / 0.329 | 0.537 / 0.543 / **0.546** / 0.540 |\n| \\$\\lambda\\_d\\$ |  0.1 / 1.0 / 10 / 20 | 17.82 / **18.04** / 17.85 / 17.87 | 0.324 / **0.315** / 0.318 / 0.323 | 0.545 / **0.553** / 0.549 / 0.531 |\n| \\$\\lambda\\_g\\$ | 0.001 / 0.005 / 0.01 / 0.1 | 17.76 / **18.22** / 18.16 / 17.88 | 0.312 / 0.310 / **0.309** / 0.311 | 0.536 / **0.548** / 0.547 / 0.540 |\n\nWe varied key hyperparameters ($\\beta_p$ is derived from $\\eta$) over a range. Results show only moderate metric changes. For example, $\\alpha$ from 0.05 to 0.6 alters PSNR by <0.3 dB, with minimal perceptual impact. Other hyperparameters show similar stability, indicating strong robustness to tuning. We also evaluated two structural parameters on both **LSRW** and **LMIE**:\n\n| Method | PSNR ↑ | SSIM ↑ | LPIPS ↓ | NIQE ↓ | PI ↓  |\n|------------|--------|--------|---------|--------|--------|\n| $k = 1$ | 16.23  | 0.481  | 0.341   | 4.31   | 4.19 |\n| $k = 2$ | 17.38  | 0.517  | 0.392   | 3.88   | 3.48 |\n| $k = 3$ | 18.12  | 0.545  | 0.297   | 3.11   | 2.96  |\n| $k = 4$ | 18.24  | 0.551  | 0.293   | 3.14   | 3.02|\n| $N = 90$   | 16.21  | 0.480  | 0.391   | 4.29   | 4.21 |\n| $N = 100$  | 18.10  | 0.542  | 0.296   | 3.16   | 3.02 |\n| $N = 110$  | 18.26  | 0.546  | 0.300   | 3.13   | 2.99 |\n| $N = 120$  | 18.17  | 0.549  | 0.289   | 3.08   | 2.93 |\n\nPerformance improves with larger $k$ and $N$, but saturates beyond $k{=}3$ and $N{=}100$; we adopt these as defaults for a quality-efficiency trade-off. Results show our method is robust and stable across a wide hyperparameter range, indicating strong generalization.\n\n**All supplementary tables and visualizations will be included in the final version of the paper or appendix.**"}, {"review_id": "IcgHh6Sbkg", "reviewer": "Reviewer_S427", "summary": "The paper presents Luminance-Aware Statistical Quantization (LASQ), an unsupervised framework for low-light image enhancement that treats illumination correction as a statistical sampling task rather than a direct pixel mapping. LASQ first shows, through empirical analysis, that natural low-to-normal luminance transitions follow power-law density curves; it then builds hierarchical “luminance adaptation operators” by sampling these curves with an adaptive Markov chain Monte Carlo strategy. These operators guide the forward path of a diffusion model, letting the network learn global-to-local corrections without paired supervision, while an optional adversarial head can exploit normal-light references when they exist.", "questions": "1. The framework relies on accurate local luminance statistics, heavy sensor noise or clipping might break the power-law assumption. Has the author considered this question?\n\n2. Can the author provide a more adequate ablation experiment to verify the impact of the proposed method and the selection of hyperparameters on the results?\n\n3. The optional adversarial head is said to improve texture fidelity when paired references are available; how much benefit does it add in strictly unpaired training, and does it introduce any instability during optimization?\n\n4. The paper shows that LASQ slightly lags supervised diffusion models on PSNR for the LOL dataset; have you identified specific error patterns that explain this gap, and how might they be addressed?\n\nI will adjust my score as appropriate according to the author's reply.\n5. Could you provide failure examples involving extreme dynamic range, motion blur, or LED flicker, and discuss whether the current framework can be adapted to handle such cases?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The method is rooted in a measurable physical regularity, the power-law distribution of luminance, which gives clearer interpretability than black-box intensity mappings.\n\n2. Hierarchical sampling plus diffusion allows the same network to operate with or without reference images, improving generalization across sensors and scenes.\n\n3. Quantitative results show state-of-the-art no-reference scores and competitive full-reference scores, backed by qualitative comparisons that highlight reduced over-exposure and noise.\n\nWeaknesses:\n\n1. The diffusion process uses 1,000 steps, so inference may slower and costlier than baselines.\n\n2. Several hyper-parameters (α, η, δ, number of layers) are hand-tuned; stability under different cameras or unseen lighting is not fully explored.\n\n3. The paper claims “physics-aware” operation, yet provides limited analysis of failure modes under extreme lighting, motion blur, or raw sensor data.\n\n4. The ablation experiment is insufficient.", "was_revised": true, "final_justification": "I have read the authors’ response as well as the concerns raised by most of the reviewers. The explanations provided have given me greater confidence in the effectiveness of the proposed method, and I have adjusted my score accordingly. I also recommend that the authors further improve the figures and descriptions in future versions, and use vector graphics to enhance readability.", "rebuttal": "We sincerely thank the reviewer for their valuable feedback and for giving us the opportunity to further explain and improve our work. We address each of your comments in detail below, and we hope that our responses resolve any questions or concerns you may have.\n\n**Q1: Robustness Beyond the Power-Law Assumption**\n\nWe would like to clarify that our approach employs a coarse-to-fine MCMC sampling strategy to fit the luminance adaptation space, rather than rigidly enforcing the power-law assumption. As discussed in Appendix A (paragraph 4), we reinterpret the strict power-law model as a relaxed prior and leverage MCMC sampling—integrated with the forward process of a diffusion model—to explore the distribution space.\n\nThis design enables LASQ to flexibly capture a wider family of luminance mappings that are only loosely guided by the physics-inspired prior, rather than being constrained to a fixed analytical form. As a result, our method does not critically depend on the power-law assumption itself. The MCMC-based inference operates at a structured level, incorporating spatial context, which allows it to remain robust under spatially inconsistent or extreme lighting conditions—well beyond the capabilities of per-pixel or strictly model-driven approaches.\n\nChallenging scenarios such as extreme dynamic range and heavy sensor noise, as you mentioned, will be specifically evaluated in the following rebuttal. The corresponding quantitative results further demonstrate the robustness of LASQ under these conditions.\n\n**Q2 & W2, W4: Hyperparameter Selection and Ablation**\n\nWe conducted a hyperparameter sensitivity analysis on the **LSRW** dataset :\n\n|Param| Value| PSNR ↑ |LPIPS ↓| SSIM ↑ |\n|-| -|-|-|-|\n|\\$\\alpha\\$|  0.05 / 0.15 / 0.3 / 0.6| 17.81 / **18.10** / 17.92 / 17.84| **0.319** / 0.322 / 0.320 / 0.324| 0.512 / **0.543** / 0.530 / 0.519 |\n|\\$\\eta\\$|   0.1 / 1.0 / 3.0 / 6.0| 17.85 / **18.35** / 18.17 / 17.95 | 0.335 / **0.321** / 0.324 / 0.329| 0.537 / 0.543 / **0.546** / 0.540 |\n|\\$\\lambda\\_d\\$|  0.1 / 1.0 / 10 / 20| 17.82 / **18.04** / 17.85 / 17.87| 0.324 / **0.315** / 0.318 / 0.323| 0.545 / **0.553** / 0.549 / 0.531 |\n|\\$\\lambda\\_g\\$| 0.001 / 0.005 / 0.01 / 0.1| 17.76 / **18.22** / 18.16 / 17.88 | 0.312 / 0.310 / **0.309** / 0.311 | 0.536 / **0.548** / 0.547 / 0.540 |\n|\\$N\\$|90 / 100 / 110 / 120| 16.21 / 18.10/ **18.26** / 18.17| 0.391 /0.296 / 0.300 /**0.289** | 0.480 / 0.542 / 0.546 /**0.549**|\n\nWe varied key hyperparameters ($\\beta_{p}$ is determined by $\\eta$ and $\\delta$, where $\\delta$ is a variance stabilization factor typically set to 0.001) over a range. Results show only moderate metric changes. For example, $\\alpha$ from 0.05 to 0.6 alters PSNR by <0.3 dB, with minimal perceptual impact. Performance improves with larger $N$ but saturates beyond $N{=}100$, which we adopt as the default for a quality-efficiency trade-off. Results show our method is robust and stable across a wide hyperparameter range, indicating strong generalization.\n\nWe also conducted more ablation studies using both **LSRW** and **LMIE**:\n\n| Method | PSNR ↑ | SSIM ↑ | LPIPS ↓ | NIQE ↓ | PI ↓ |\n|-|-|-|-|-|-|\n| $k = 0$ |15.82|0.447|0.429|4.51|4.35|\n| $k = 1$ |16.23|0.481| 0.341| 4.31|4.19|\n| $k = 2$ |17.38|0.517| 0.392| 3.88| 3.48|\n| $k = 3$ |18.12|0.545| 0.297| 3.11| 2.96|\n| $k = 4$ |18.24|0.551| 0.293| 3.14| 3.02|\n|w/o $\\mathcal{L}_{\\text{g}}$|17.93|0.460|0.334|3.21|3.13 |\n|Default|18.14|0.547|0.308|3.15| 3.00|\n\nWe performed ablation studies to evaluate key components of LASQ. The case of $k=0$, corresponding to enhancement in pixel space, yields the weakest results—validating the effectiveness of our latent-space design. Additionally, removing $\\mathcal{L}_{\\text{g}}$ leads to clear performance drops, confirming its importance to the final quality.\n\n**Q3: Effectiveness of the Adversarial Head in Unpaired Settings**\n\nWe would like to clarify that the introduced adversarial discriminator is applied to unpaired references. In fact, the LASQ++ variant in our main paper is trained strictly with unpaired references, and the adversarial head still provides clear improvements in texture fidelity under this setting. We will revise the manuscript to better emphasize this point and avoid possible misunderstandings. Importantly, the adversarial discriminator does not introduce any instability during optimization, thanks to the appropriate choice of the weighting parameter $\\lambda_{GAN}$. The detailed performance metrics are provided below.\n\n| Method| LOL(PSNR / SSIM / LPIPS) | LSRW(PSNR / SSIM / LPIPS) |\n| -| -| -|\n|LASQ| 20.375 / 0.814 / 0.191|18.137 / 0.547 / 0.308 | \n|LASQ ++| 20.481 / 0.807 / 0.205|18.584 / 0.540 / 0.316| \n\n**Q4: Generalization Limits and Adaptive Strategies on LOL Dataset**\n\nLASQ has demonstrated strong generalization ability and stability, surpassing some supervised methods on datasets such as LSWR, DICM, NPE, and VV. However, in the LOL dataset, where the training and testing images often come from similar scenes, supervised paired-learning methods can better fit the overall data distribution. Since LASQ does not utilize any reference images during training, its performance on certain metrics may be lower than that of supervised approaches. We analyzed the error maps and found that the primary issues are concentrated in a few images, where LASQ tends to produce less detailed textures and shows a global luminance discrepancy compared to the ground truth. \n\nAs a diffusion-based generative model, LASQ may occasionally generate overly smooth results. To address this, we plan to improve the decoder’s sensitivity to fine details by incorporating multi-scale texture refinement modules. Additionally, we will introduce a structure-aware loss function designed to explicitly penalize over-smoothing in regions with high structural complexity.\n\nFurthermore, since LASQ operates without any reference images, it may lack awareness of the specific luminance distribution present in the LOL dataset. To mitigate this, we propose to anchor the MCMC sampling process around the dataset's typical luminance domain, using it as a prior to better align the generated outputs with the illumination characteristics of LOL images.\n\n**Q5 & W2, W3: Evaluation Analysis under Challenging Conditions**\n\nWe first evaluated LASQ on “raw images” captured by three “different camera” models in the **ELD** dataset. The results show that LASQ achieves consistently strong performance across all camera sources, suggesting that our approach is not sensitive to differences in camera hardware or imaging pipelines. In contrast, other methods exhibit significantly less stability.\n\n| Camera| CanonEOS70D (PSNR / SSIM / LPIPS) | NikonD850(PSNR / SSIM / LPIPS) | SonyA7S2 (PSNR / SSIM / LPIPS) | \n| -| -| -| -|\n|PairLIE| 17.45/ 0.485/0.524|16.77/0.428/0.591|17.10/0.462/0.541|\n|WCDM| 17.29/ 0.560/0.514|16.80/0.443/0.536|17.17/0.555/0.539|\n|**LASQ**| **17.75**/ **0.602**/**0.411**|**17.83**/**0.598**/**0.417**|**17.90**/**0.611**/**0.420**|\n\nTo further examine robustness under challenging conditions, we applied LASQ to three representative datasets: **ELD**, which contains “raw sensor data” captured under “extreme dynamic lighting”; **LOL_blur**, which includes low-light images degraded by “motion blur”; and **LED**, which comprises low-light images affected by “LED flicker”. \n\n| Method| ELD (PSNR / SSIM / LPIPS) | LOL\\_blur (PSNR / SSIM / LPIPS) | LED (NIQE / PI) |\n| -| -| -| -|\n| EnlightenGAN| 16.78 / 0.485 / 0.539| 16.15 / 0.537 / 0.591| 3.648 / 3.506 |\n| KinD++| 12.48 / 0.310 / 0.888| 17.88 / 0.526 / 0.523  | 3.645 / 3.323 |\n| LightenDiffusion| 13.66 / 0.364 / 0.826| 18.18 / 0.643 / 0.459 | 3.550 / 3.661|\n| NeRCo| 10.29 / 0.260 / 0.714| 16.82 / 0.645 / 0.447| 3.546 / 3.282|\n|PairLIE| 17.45 / 0.485 / 0.524| 17.35 / 0.616 / 0.457| 3.848 / 3.907|\n|SCI| 14.53 / 0.149 / 0.568| 12.74 / 0.430 / 0.637 | 3.961 / 3.268|\n|SCL-LLE| 14.66 / 0.158 / 0.547| 11.57 / 0.423 / 0.631 | 3.737 / 4.225|\n|URetinex-Net| 16.73 / 0.463 /0.544| 17.39 / 0.634 / 0.420| 3.692 / 3.306 |\n|**LASQ (Ours)**| **17.69** / **0.610** / **0.409**|**19.11** / **0.648** / **0.418** | **3.545**/ **3.257**|\n\nAlthough performance metrics are slightly reduced compared to standard scenes, LASQ still achieves the best results across all three challenging conditions. This highlights its robustness and adaptability to real-world failure modes. In particular, the relatively strong performance under motion blur and LED flicker suggests that the proposed physics-aware design helps mitigate degradation caused by non-ideal sensor inputs and dynamic lighting effects, without relying on explicit temporal modeling or post-processing.\n\nUnder extreme conditions—such as low illumination or severe motion blur—our original framework showed limitations in preserving fine textures and accurate brightness. We will explore enhancing the decoder with multi-scale texture refinement and anchoring the MCMC sampling distribution to the ground truth luminance characteristics observed in extreme cases.\n\n**W4: Inference Computation**\n\nWe compare LASQ with early lightweight methods (e.g., EnlightenGAN, KinD++) and recent diffusion models (e.g., WCDM, LightenDiffusion). While diffusion models outperform early methods, they are computationally heavy. LASQ retains their performance but offers much higher efficiency without relying on reference images, making its moderate overhead a practical trade-off.\n\n|Method|Inference Time (ms)|Memory Usage (MB)|PSNR ↑|\n| -| -|-|-|\n|EnlightenGAN (2019)   |170.16 |241.48|17.606|\n|KinD++ (2021)|4279.70|372.19|17.752|\n|NeRCo (2023)|354.77 |2320.87|19.738|\n|PairLIE (2023)|190.70|3499.79|19.514|\n|WCDM (2023)|206.66|6017.86|20.105|\n|LightenDiffusion (2024)|257.94|8049.95 |20.453|\n|LASQ (ours)|213.89|6496.68|20.481|\n\n**All supplementary tables and visualizations will be included in the final version of the paper or appendix.**"}]

Author rebuttal:

Meta-review: The paper proposes LASQ, a zero-reference low-light image enhancement method that reframes luminance adjustment as a multi-scale, physics-informed statistical process within a diffusion framework. It achieves state-of-the-art results on paired and unpaired benchmarks. Reviewers praised the contribution and empirical quality; initial concerns about hyperparameter tuning and efficiency were resolved during rebuttal. I recommend acceptance.
Decision: Accept (poster)