Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios
Authors: Li Huaqiu
HuXiaowan
Haoqian Wang
Venue: ICLR 2025 Poster (2025)
Keywords: unsupervised learning
low-light image enhancement
image denoising
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: An interpretable joint method for image denoising and enhancement

Abstract: Real-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Supervised methods tend to overfit to specific scenarios, while unsupervised methods, though better at generalization, struggle to model these degradations due to the lack of reference images. To address this issue, we propose an interpretable, zero-reference joint denoising and low-light enhancement framework tailored for real-world scenarios. Our method derives a training strategy based on paired sub-images with varying illumination and noise levels, grounded in physical imaging principles and retinex theory. Additionally, we leverage the Discrete Cosine Transform (DCT) to perform frequency domain decomposition in the sRGB space, and introduce an implicit-guided hybrid representation strategy that effectively separates intricate compounded degradations. In the backbone network design, we develop retinal decomposition network guided by implicit degradation representation mechanisms. Extensive experiments demonstrate the superiority of our method. Code will be available at https://github.com/huaqlili/unsupervised-light-enhance-ICLR2025.

Published as a conference paper at ICLR 2025
INTERPRETABLE UNSUPERVISED JOINT DENOISING AND ENHANCEMENT FOR REAL-WORLD LOW-LIGHT SCENARIOS
Huaqiu Li, Xiaowan Hu, Haoqian Wang Tsinghua Shenzhen International Graduate School Tsinghua University (lihq23, hu-xw19; i 1s. www.mml.ssingiiiee............ www.rsiiiiga......uu.u
ABSTRACT
Real-world low-light images often suffer from complex degradations such local overexposure, low brightness, noise, and uneven illumination. Supervised meth- ods tend to overfit specific scenarios, while unsupervised methods, though better at generalization, struggle to model these degradations due to the lack of reference images. To address this issue, we propose an interpretable, zero- reference joint denoising and low-light enhancement framework tailored for real- world scenarios. Our method derives training strategy based on paired sub- images with varying illumination and noise levels, grounded in physical imag- ing principles and retinex theory. Additionally, we leverage the Discrete Cosine Transform (DCT) perform frequency domain decomposition in the sRGB space, and introduce an implicit-guidee hybrid representation strategy that effectively separates intricate compounded degradations. In the backbone network design, we develop retinal decomposition network guided by implicit degradation rep- resentation mechanisms. Extensive experiments demonstrate the superiority of our method. Code will be available at https::tt /ttpstts:///i/tiie h/tpt//gg.ttttt//////pp thub...ub.... wtw.tthwoooooooo... hoom/huaqlile 1il/ cewpppervieee-dade (Jnsupervi] sed- 1endd-iight iight -ene-ennaancee TCLR2025
INTRODUCTION
Low-light image enhancement is significant research area in computer vision and image process- ing. The inherently low signal-to-noise ratio of such images can adversely impact downstream tasks, such object detection Rashed (201)) image segmentation Wang al. (2022), and face recog- nition (Serengil & Ozpinar (2020). Moreover, the widespread application of low-light enhancement in fields like nighttime photography Jin al. (2022) (023). astronomical observation Chen et al. (022I) and autonomous driving Li et al. (2024) underscores its critical importance in low-level vision tasks.
Real-world low-light enhancement presents numerous challenges, requiring simultaneous handling of issues such as brightness. contrast, artifacts, and noise. Over the past few decades, traditional methods like gamma correction Huang et al. (2012), histogram equalization Lee et al. (2013), and retinex theory Land & McCann (1971) have been developed. However, these methods focus on single-dimensional brightness issues and struggle with complex real-world scenes, while their hand- crafted priors often lack generalization for diverse conditions.
In recent years, learning-based methods for low-light enhancement have achieved significant progress. However, these approaches often rely on paired (e.g. Zhang et al. (202T); Cai et al. (0033): Wu et al. (2022); Xing al. (2024); Bai et al. (2024)) unpaired (c.g. Jiang et al. (2021 Yang et al. (2023) data, making it challenging to collect large-scale datasets. Additionally, discrepan- cies in brightness between reference images can disrupt model fitting, making the development of efficient zero-reference methods crucial.
Current zero-reference low-light enhancement methods, such -eero-DEEGGane al. 20000) utilize
curve learning for iterative optimization, but does not account for noise degradation. Approaches like SCI Ma et al. (2022) and RUAS Liu al. (2021) follow similar iterative strategy, integrat- ing denoising modules. However, while separate denoising modules are designed for end-to-end
"oorrespnniing author.

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Published as a conference paper at ICLR 2025
Input
Clip-LIT
SCI
Ours
Figure Compared with state-of-the-ar methods Liang et al. 2023 Ma et al. (2022) the SIDD dataset, our approach achieves the best results in denoising, enhancement, and color fidelity grounded in real-world imaging principles.
training, they rely on specific, lengthy loss functions that lack generalization across various noise patterns. Other methods Fan al. (2022) address multiple degradation tasks through multi-stage learning. They often overlook the error accumulation during the optimization process (e.g., noise becomes more complex after low-light enhancement). Furthermore, Fig shows, these methods generally fail to differentiate feature layers for multiple degradation modes, leading to confusion and ambiguity during the restoration process.
To address the aforementioned challenges, we propose a zero-reference joint denoising and enhance- ment method grounded in real-world physical models. Specifically, we introduce self-supervised image denoising method based on neighboring pixel masking, alongside a self-supervised enhance- ment strategy that combines random gamma adjustment with retinex theory. By obtaining sub-image pairs with varying illumination and noise levels, the framework is capable of tackling the complex degradation issues caused by low-light conditions. Additionally, we employed DCT to model phys- ical priors that reflect various degradations, and designed global learning-basee encoder to extract implicit degradation representations from them. In the backbone network design, we develop retinal decomposition network guided by implicit degradation representation mechanisms. This approach allows us to separate and address complex degradations the frequency domain, rather than se- quentially handling features as in previous methods. Extensive experiments demonstrate that our method offers significant advantages over the current SOTA approaches.
The main contributions of this paper are follows:
By preprocessing the original low-light image generate paired sub-images with varying illumination and noise levels. followed by retinal decomposition, we derived and validated a physically sound unsupervised joint denoising and enhancement framework.
We utilized DCT to model physical priors that capture intricate compounded degradations, and designed globally learned encoder to extract implicit degradation representations from these priors
We developed hybrid- priidd-rroorooo attention transformer network that integrates degradation fea- tures reconstruct the reflection map, while adaptively enhancing the illumination.
Extensive experiments multiple real-world datasets demonstrate that method achieves superior performance across several metrics compared to SOTA approaches.
RELATED WORKS
2.1 SELE-SUPERVISSDD/UNSSPEEVVE LOW-LIGITT IMAGE ENIIACCEEET
The development of self-supervised and unsupervised low-light enhancement follows two main ap- proaches: zero-reference and unpaired learning. Zero-DCE Guo et al. (2020) introduced curve- based iterative method for zero-reference enhancement, later refined by Zero-DCE++ Li et al. (202I) for better efficiency. Methods like RUAS Liu et al.] (2021) and SCI Ma et al. (2022) ex- tend this approach with denoising modules for handling complex degradations. However, these approaches often struggle with interpretability and modeling complex degradations. In contrast, unpaired learning leverages low-light and normal-light image pairs from different scenes varying illumination within the same scene. GAN-based methods like EnlightenGAN Jiang et al. (202T) and NeRCo Yang al. (2023) use cyclical networks for bidirectional image transformation learning be- tween domains. PairLIE Fu et al. 2203) processes low-light images with varying degradations from the same scene using retinal theory. Although these methods demonstrate strong generative abili- ties, their performance can be constrained by inconsistent normal- light references and difficulties in normalizing illumination distributions.
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2.2 FREQUENCY-DOMAIN ANALYSIS IMAGE PROCESSING
DCTconv Cheiński & Wawrzynski 2020) integrates convolution with IDCT to form a novel layer that facilitates network pruning. Xie et al. (021) introduces frequency-aware dynamic network that leverages DCT in image super-resolution to reduce computational cost. To improve content preservation, Cai et al. (202)) employs Fourier frequency spectrum consistency constraint for image translation. Recently, frequency domain processing has gained significant attention. Zou al.
2024) demonstrates that degradation predominantly affects amplitude spectra, while FSI et al. restoration. Zou al. (2022) employs wavelet transforms to disentangle frequency domain infor- mation, using multi-branch network to recover freufrryyenccy details. WINNet Ou et al. (2024) combines sevelet-based and bearning-basee methods to construct reversible, interpretable network with strong generalization capabilities. FCDiffusion Gao et al. (2024) utilizes DCT to filter feature maps, achieving controlled generation across different frequency bands.
2023) designs a frequency-spaan interactive network to address under-display camera image
METHOD
3.1
THEORETICAL BASIS
3.1.1 RETINEX THEORY
The traditional Retinex image enhancement algorithm Land & McCann (1971); Wei al. (2018) simulates human visual perception of brightness and color. It decomposes image E R xx x into the illumination component L E "Xx33 x3 and the reflection component R " This conclusion can be expressed by the following formula:
RoL
where o denotes element-wise multiplication. The reflection component It is determined by the in- trinsic properties of the object, while the illumination component L represents the lighting intensity. However, the traditional Retinex algorithm does not account for complex degeneration produced by unbalanced light distribution or real-world dark scenes in low-light conditions, and this loss of qual- ity is further amplified with the enhancement of the image. Therefore, we add the noise disturbance term N on the reflection component as the basis of theoretical analysis:
I = (R + N) L
(2)
In most low-light scenarios, is modeled as zero-mean Poisson noise.
3.1.2 NEIGHBORING PIXEL MASKING IN SULE-SUPERVIEED DENOISING
Image denoising represents classic ill posed problem within the domain of image restoration. This signifies the existence of multiple potential solutions for the same noisy scene. Previous image denoising models Zhang et al. (2017); Goyal et al. (2020) typically require paired input of noisy images y; and corresponding clean images x; to train the network effectively.
arg min Σ [((fo(y)).x))
Here, 9 represents parameters that need be optimized. However, in practical scenarios, obtaining paired images is often challenging even impossible. As result, series of self-supervised and unsupervised methods have emerged utilizing only noisy images for training.
The theoretical foundation of N2NLehtinen et al. (018) is rooted in point estimation, which esti- mates the true value of series of observations [x1, X2, ... X.). The objective to find a value that minimizes the sum of distances to all the observed values, serving as the estimation. When using L2 loss for estimation, replacing x with another observation z having the same mean value does not alter the result.
Extending this theoretical point estimation framework to training neural network regressors, the optimization objective of the network can be transformed into:
arg min Σ L(fo(y:),
(4)
3

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PartA: Framework
LCact
12(00
R,OL
Devinpsee
Dz(I)
Dz(1)
R2 O
R₂O 4g
PartB: Decompose-Net
LUMaet
REFnnt
Seat
Saaie
Scale
Smale
Muli Read Atteniten
P,0 3,0
D2O)
Allesiion
Noem
R₁
D20)
Len
oontinal
PartC: DCT Module
lluinnatio Piror
Hightpos Price
Lowpus Filter
Inverse DCT
Flender
Space
Space
Highpass brlter
Masking
Figure 2: The pipeline of our proposed method: image / using pixel masks and gamma-based nonlinear enhancement, generating sub-images with varying illumination and noise levels. These are then processed through Decompose-Net, which uses transformer architecture integrating hybrid degradation representations, incorporating cross- attention inject guiding embeddings. Subsequently, LCnet enhances the illumination map.
First, preprocess the low-light full-resolution
This implies that when training a denoising network, if we replace the clean images x; with noisy images which have zero-mean noise, the optimization results using L2 loss will be equivalent those trained by pairs of noisy-clean images. This assumption forms the foundation of our work.
3.2 OVERALL ARCHITECTURE
Building on the aforementioned theoretical foundation, we express low-light image I (R + N) o L, where N represents zero-mean noise distribution. Our objective to generate images of the same scene with differing noise observations, ensuring that the noise remains zero-mean and the denoised ground truth consistent across these images. In scenarios where a normal-light reference image unavailable, of neighboring masking D. Specifically, the original image / is partitioned into multiple 2x2 pixel patches. From each patch, two adjacent pixels are randomly selected and assigned to corresponding regions in the two sub-images. The resulting sub-images can thus be mathematically formulated as:
propose to generate two sub-images at 1/4 resolution through process
(1()) = (R1 + N1) I,, D2(T) = (R2 + N₂) L2
(5)
Here, N2 and N2 represent noise components that follow shared distribution, R₁ and R2 are highly similar in pixel values, and L, and La correspond the same lighting conditions.
Previous study Fu et al. (023) has indicated that if images of the same scene under different illumi- be obtained, deep learning can be employed to decompose the corresponding reflectance R, with the principle that the reflectance Ri and R2 should theoretically be identical. To generate a supervision signal with different illumination,
nation conditions
apply gamma correction D2(() and get (T). We avoid applying gamma correction directly the original image / because the noise N would be preserved at nearly the same level, making the network learn an identity mapping. After obtaining the enhanced image D2(l). and given that N₂ relatively small compared the pixel values, we further perform a Taylor series expansion on it:
D2(1) D (Rg N2) LA (R) AR. N2) L3 (Re + AN2) R
LI (6)
Here, À represents the gamma enhancement factor, and R x 1 when À is close to The original equation thus be further rewritten (R2 + XN2) T.z, T. T.2. leading the final expressions

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Published as a conference paper at ICLR 2025
head x c Aee
head & x
E
x x
C
F
P
Resheup
CXNXx W
bead x Aw
Matrix Multiplication
Figure 3: Illustration of the hybrid prior degradation representation guided by multi- cross attention. After processing, the feature maps exhibit clearer hierarchical structure and reduced noise. for the two images:
DI(1) = (R) + Ni) L1, D2(1) (R2 + AN2) L2
In this formulation, R1 and Rg share the same ground truth reflectance, they exist within the same scene. Meanwhile, N1 and AN2 represent zero-mean noise distributions that are non-identical. Additionally, the first and second images encompass different illumination conditions. Therefore, by simply constraining (R₂ + N2) and (R2 + AN2) to be equal, we can construct a self-supervised network jointly performing denoising and enhancement (DEnet).
As illustrated in Fig 2 the overall architecture of DEnet is primarily divided into four components: the Frequency-lllmmnnatio representation Encoder (Flcoder), the Reflectance Map Extraction Net- work (REFnet), the Illumination Map Extraction Network (LUMnet), and the Light Correction Net- work (LCnet). REFnet and LUMnet are employed to extract the reflectance maps R1, Rg, and illumination maps L1, Ln from sub-images D2 (T) and (7). In LUMnet, each transformer block divided into a self-attention computation module and a gating module. In contrast, REFnet, tasked with reflectance map extraction, requires the degradation representations to perform cross-attention calculations with feature tokens ss illustrated in the Fig LCnet processes its features using a trans- former and then applies global average pooling. The pooled features are passed through two linear layers to scale them into a one-dimensional enhancement factor to correct the illumination map, which is subsequently multiplied with the reflectance map produce the final corrected image Len-
3.3 FREQUENCY-ILLUMINANNN
PRIOR ENCODER
Ficoder is primarily designed to obtain degradation representations from illumination and fre- quency domain priors, which are then integrated with feature maps through cross-attention mech- anisms in REFnet. The fusion of multiple priors enhances the model's generalization capa- bility across diverse and complex degradations.
illustrated in the Fig/4] the illumination prior Ih represents the image's luminance information, while the four frequency domain priors Clow Clow Chigh_1- Chigh_2, 2, ranging from low to high frequencies, capture information on chromaticity, semantics, edge contours, and noise intensity, respectively.
First, we extract the illumination prior Iim me)nn(T), which the mean value of the sub- image across the channel dimension, representing the overall brightness level of the image. As for frequency prior, we use channel-wise 2D DCT to convert the spatial-domain image into the frequency -ommann counterpart F. Different spectral bands in the DCT domain encode different im- age visual attributes degradation representation analysis of input images. To obtain the frequency spectrum maps across four frequency bands, define four masks:
Moo 1 (u, v) = 1 iʃ u u S L else 0, Moo 2(u, u) - is u uoos 31 else O, Mhigh_ (1, =1 if 2 4t else O, Mhigh M u), =1 if u 5 else 0.
(8)
(9)
F = FM"
(10)
where * E [low 1, low_2w2222 high_ 1, high 2)__22,,-22_ and t represents the manually set bandwidth hyperpa- rameter. We apply the masks M,, to the frequency spectrum feature maps F to filter them according to different frequency bands. By performing an inverse Discrete Cosine Transform (IDCT) on these filtered maps F., we obtain the corresponding spatial domain feature images C.,
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1u
Clow11
Ciow22
Crigh
Crigh
Figure 4: The visualization of the five image priors. They represent chromaticity, semantic informa- tion, edge contours, and noise intensity.
Finally, we combine La E B#xxx1, Clow Clow 2, Chigh_ and Chigh_ # through convolutional network-base Illumination-Fe Freeuuenn Prior Encoder. This encoder constructs the implicit representation Pe R #xxxC based on separating degradation features. During training, the Flcoder processes the input sub-images (7) and D2 /), generating the corresponding degra- dation representations P1 and Pz.
3.4 LOSS FUNCTION
During model training, DEnet performs the following computations:
Ten (F R1 L. R1 REF (T), Pı), In (U), = (11)
During inference, we input the original-resollutin low-light image I, multiply the decomposed
flection component with the corrected illumination L, and obtain the final enhanced result.
The loss function for this method is primarily divided into two aspects: 1) Retinex Decomposi- tion Loss: This loss constrains the retinex decomposition to ensure that the resulting reflectance and illumination maps are consistent with the underlying physical assumptions. 2) Self-supervised Enhancement Loss: This loss is designed to regulate the enhanced image Jen by imposing con- straints brightness, contrast, saturation, and other factors, ensuring that the enhancement aligns with desired visual qualities.
The Retinal Decomposition Loss we employ is primarily divided into two parts: the first is LR. as mentioned earlier, which primarily constrains the L2 distance between the reflectance maps Rū and Rg derived from 1 and (T); the second is LL, which imposes smoothness constraints on the illumination maps and ensures that the product of the decomposed maps equals the original image. The expressions for these two losses are shown as follows:
Ln = (T) - REF
P2) 2 Wregfreg
(12)
D1(1) de.
Lr. R o - |[, Loll2 R1
+ VVL, La = max Di CE (r.g.))
(13) Here, P1 and P2 represent the degradation representations extracted by the Flcoder from the sub- images Dj (I) and D2()), respectively. VL1 denotes the gradient of the illumination map. We add a regularization term Greg to align gradients and test the original-scale images. The masked testing results compared with sub-image reflectance maps via Lg-norm, ensuring the consistency of R and R2 across scales, enhancing generalization and training stability.
(1), Pi) Lreg ()(T::/())))--EE/D))))))))-)--
P)) ())1 (14)
For the Self-supervised Enhancement Loss, we designed two components: the consistency loss Loon and the enhancement loss Lenhi
Loon
Σ Σ
(1Len.s
-
- Di())))
(15)
K
K
1=1 (Ea())
1 Lenh = Weap K
Ien,i
El + Weol
Σ - {(R, G). B). B)} (16) v(p.g)Ee
The images before and after enhancement are divided into K patches. Here, (1) represents the neighboring patches surrounding position i. Ten,i and Di (T); denote the mean pixel values within

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the i-th patches at the corresponding position. The loss Lenh imposes constraints the average brightness of the patches and the overall chromaticity of the image, where Vp denotes the average intensity value of channel in the enhanced image, and E represents the exposure standard that aligns with natural perception. Weap and Weal represent the respective weighting factors. Finally, the overall loss of the end-to-end network can be described as follows:
C WRCR + WICL + WoonCcon Wenh
(17)
Here, WR, WL, Weon, and Wenh represent the respective weighting factors.
Table 1: PSNR1, SSIM,, LPIPS scores on the image sets (LOLv1, LOLv2). The best result is in red, whereas the second-best one is in blue under each case.
LOLvI SSIM
LOLv2-Rea SSIM
Method
Reference
PSNR
LPIPS
PSNR
LPIPS,
Supervised
URetinexNet
Wu ct al. 2022
19.84
0.824
0.237
21.09
0.858
0.208
SNR-aware
Xu
2022
24.61
0.842
0.233
21.48
0.849
0.237
LLFormer
Wang al 202.
23.65
0.818
0.169
27.75
0.861
0.142
Retinexformer
Cai el al. 20233
23.93
0.831
21.23
0.838
Retinexmamba
Bai ct al.
2024
24.03
0.831
22.45
0.844
Unpaired
EnlightenGAN
Jiang et al. (2021
17.48
0.651
0.322
18.64
0.675
0.308
PairLIE
et al
2023
19.51
0.736
0.247
19.70
0.774
0.235
Nerco
Yang et a.
2023
19.70
0.742
0.234
19.66
0.717
0.270
No-Reference
ZERO-DCE
Guo
2020
14.86
0.559
0.335
18.06
0.573
0.312
RUAS
Liu el al
2021
16.40
0.500
0.270
15.33
0.488
0.310
Sci-easy
Ma el al.
2022
9.58
0.369
0,410
11.98
0.399
0.354
Sci-medium
14.78
0.522
0.339
17.30
0.534
0.308
Sci-hard
13.81
0.526
0.358
17.25
0.546
0.317
Clip-LIT
Liang 2023
17.21
0.589
0.335
17.06
0.589
0.352
Enlighten- Your- Voice
Zhang ct al.
2025
19.73
0.715
19.34
0.686
Ours
19.80
0.750
0.253
20.22
0.793
0.266
Table PSNRYSSSMMYLLIISS, scores on the image set SICE, and BRSIQUEL/CCLPIIOA scores
on the image SIDD. The best result is in red, whereas the second-best one is in blue.
SICE
SIDD BRSIQUEL CLIPIQAL
Method Supervised
Parameters
PSNR
SSIM
LPIPS
URetinexNet
1.04M
22.12
0.844
0.462
ANR-aware
50.95M
15.02
0.584
0.527
25.679
0.294
LLFormer
72.29M
17.88
0.821
0.503
3.548
0.339
Retinexformer
1.61M
9.229
0.343
Retinexmamha
4.59M
11.826
0.386
Unpaired
EnlightenGANN
8.44M
18.73
0.822
0.216
13.786
0.337
PairLIE
0.34M
21.32
0.840
0.216
3.168
0.383
Nerco
22.76M
18.72
0.805
0.474
No-Reference
ZERO-DCE
0.08M
18.69
0.810
0.279
24.291
0.503
RUAS
0.01M
13.18
0.734
0.363
31.613
0.361
Sci-easy
0.01M
11.71
0,590
0.502
25.344
0.399
Sci-medium
15.95
0.787
0.335
21.636
0.456
Sci-hard
17.59
0.782
0.486
35.533
0.508
Clip-LIT
0.27M 0.36M
13.70
0.725
0.480
31.093
0.434
Ours
22.55
0.841
0.234
2.555
0.292
4 EXPERIMENT
4.1 IMPLEMENTATION DETAILS
To ensure faimess, all experiments were terminated after 100 training epochs. We consistently set the initial learning rate to x 10 and conducted all experiments an RTX 3090 GPU. During training. images were randomly cropped into 256x256 patches, with pixel values normalized to the range of (0, 1), and batch size of 1 was employed.

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Input
RIAS
FnlightenGAN
Zero-DCF
SCI
PaicLIE
Nerca
Clip-LIT
Ours
Reference
Figure 5: Visual comparison of typical unsupervised enhancement methods in LOL Yang et al. 2021). Flesh pink boxes indicate the obvious differences.
Input
RUAS
EnlightenGAN
Zero-DCE
SCI
Clip-IIIT
Figure Visual comparison of typical unsupervised enhancement methods in SICE Cai et 2018). Flesh pink boxes indicate the obvious differences.
ORE
ORE
ORE
ORE
4
EFTER
ELIEN
EITER
OREE
ORE
ORE
ORE
Input
EnlightenGAN
Zero-DCE
RUAS
ORE
ORE
ORE
ORE
4
ELINN
ELICK
EIIRH
ORE
ORE
ORE
ORE
SCI
PairLIE
Clip-LIT
Ours
Figure 7: Visual comparison the real-world low-light image from the SIDD Abdelhamed et al. 2018) dataset.
We conducted tests four benchmarks: LOLv1 Wei et al. (018). LOLv2-real Yang et al. 2222 SICE Cai et al. (2018) and SIDD Abdelhamed et al. (2018). Please refer to the supplementary materials for detailed information regarding the datasets, including the corresponding training and testing splits.
4.2
BENCHMARKING RESULTS
The experimental results on the LOL dataset are presented in Tab. where our model outperforms of the compared unpaired and no-reference methods, achieving the highest scores across multi- ple metrics. The qualitative visual comparisons are shown in Fig. Unpaired methods benefit from reference images captured in normal lighting conditions, making learning the necessary illumination features casier. However, these methods struggle with underexposed local regions, leading to issues like dead black spots.
Meanwhile, EnlightGAN, ZeroDCE, and Clip-LIT successfully enhance overly dark regions. How- ever, due to the lack proper denoising mechanisms, they tend introduce noise while increasing exposure. Our approach, leveraging illumination priors and frequency domain decomposition, effec- tively compensates for multidimensional illumination information, resolving complex degradation issues such as local overexposure, underexposure, and noise.
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The experimental results the SICE and SIDD datasets shown in Tab. The selected SICE test set includes images with three levels of low-light degradation: low, medium, and high. We evaluate the generalization capability of our model under varying illumination conditions using statistical metrics, and the qualitative comparisons are shown in Fig. 6 Both RUAS and EnlightenGiAN exhibit issues such local overexposure and strong contrast distortion, which can be attributed to the lack of an interpretable illumination feedback design in their network structures. Nerco generates artifacts in certain image regions, highlighting the uncontrollabillty of generative models in image enhancement tasks. In contrast, our method demonstrates appropriate contrast, accurate chrominance, low noise, and sufficient detail.
The qualitative comparison results on the SIDD dataset are shown in Fig. 7 We assess the enhance- ment capability of our model in challenging low-light scenes with high noise levels and complex noise patterns. Our method achieves the best performance rics, BRISQUE and CLIPIQA, indicating that the enhanced images exhibit characteristics closer to natural images with fewer distortions. From the visual results, our method demonstrates robust- ness against complex noise real-world scenarios, effectively enhancing image illumination while controlling noise intensity. In contrast, other approaches either lack dedicated denoising design or handle noise from a perceptual standpoint, without corresponding theoretical analysis for inter- pretability, leading suboptimal results.
two no-reference statistical met-
Table 3: Ablation study of the contribution of the three physical priors. The best and the second best results are highlighted in red and blue.
LOLvi SSIM
LOLv2
Illumination
Lowpass
Highpass
PSNRT
LPIPS4
PSNR
SSIM1
LPIPS./
18.88
0.741
0.273
19.37
0.771
0.305
19.54
0.753
0.253
19.99
0.785
0.297
19.69
0.744
0.259
19.28
0.779
0.299
19.57
0.745
0.262
19.51
0.780
0.282
19.80
0.750
0.253
20.22
0.793
0.266
Table 4: Ablation study of the contribution of the denoising designs, where NM stands for neigh-
borhood masking. The best and the second best results are highlighted in red and blue.
LOLvI
LOLv2
Setting
NM
Greg
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
18.52
0.686
0,271
19.46
0.771
0.323
19.63
0.747
0.264
19.83
0.787
0.279
3
19.80
0.750
0.253
20.22
0.793
0.266
4.3 ABLATION STUDY
Denoiseing Design. the previous sections, we designed a hybrid mechanism combining neigh- borhood masking and gamma enhancement to construct image pairs with varying illumination and noise levels for joint denoising and enhancement training. In set we removed the masking mecha- nism and trained using the original resolution images with different illumination. In set2, applied the full preprocessing mechanism but omitted the regularization term in Equ.
14
We implemented these settings on the LOLvI and LOLv2- Real datasets, with the quantitative results presented in Tab, and the visual comparisons shown in Fig g
The results indicate that removing any part of the strategy reduces performance, and the combination of both strategies is necessary to achieve optimal denoising results. In setl, the noise intensity is significantly pronounced, primarily due to the decomposition network generating identity mappings while learning the illumination map. In set2, images lose detail in underexposed regions, which is attributed to the local semantic loss caused by downsampling.
Hybrid Piror Design. Tab. 3 and Fig. 9 present the results of the ablation study mixed priors. The priors are categorized into three parts: illumination prior, high-pass filtering prior, and low-pass filtering prior, which respectively capture brightness, noise, and color information. The results are worse when all priors are removed, with a notable improvement of approximately 0.6 dB when the illumination prior is included. On top of the full version, removing either the fighffrequency or low-frequency components adversely affects performance, demonstrating that combining multiple informative cues achieves the best results.

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Published as a conference paper at ICLR 2025
LOLv! 22n
L.Ceet
Set LOLvI p2
Set 02 LOLVI p95
Pall Verson
SICE IJGG
SICE 2JPJPG
SICB 2JJ.PG
Set LOLvi 22 png
Set 63 LOLvt 493.pmg
Figure 8: Left: design ablation.
Visualization of LCnet adaptivity experiment.
Right: Visualization of denoising
19.4
- PSNR
19.2
Trend
19
Line
18.8
Regulatory Factor
In
In + Garge
Fall Version
Figure 9: Left: PSNR variation with gamma enhancement factor on the LOLvl dataset. Right: Ablation study of different physical priors.
LCnet. The design of LCnet aims to build an illumination-adapiin module that adjusts the illumi- nation map to achieve the highest perceptual quality. We removed LCnet and employed a reference adjustment strategy similar to PairLIE. The visual results shown in Fig. g Without the adap- tive strategy, is challenging to achieve consistent enhancement results across images with varying low-light degradations from the same scene, leading to overexposure in local regions.
For the gamma enhancement operation applied to images with different illumination during pre-training, explored which enhancement factor yields the best performance. We use a to regulate through the formula = 1. . The results are shown Fig. 9 il- lustrating that the enhancement effect follows an increasing trend initially and then decreases within the a range of 1.2 to 1.9. At lower values, the enhanced images do not produce sufficient illumi- nation differences with the other sub-images, which is crucial for model decomposition. At higher values, the enhancement does conform the assumption R2--1 1 during framework infer- ence, resulting in more complex nonlinear noise variations that negatively impact model perfor- mance. Therefore, during each iteration, we randomly sample enhancement factors within the range of (1.3, 1.7) to provide the model with broader range of feature processing options. The specific selection criteria for the control factors are detailed in the supplementary materials.
Gamma Enhancement Factor.
CONCLUSION
This paper tackles the challenges of low-light image enhancement and denoising, particularly in complex real-world scenarios. We propose zero-reference framework combining self-supervised denoising via neighboring pixel downsampling and enhancement using random gamma adjustment with retinal perception theory. To address the limitations of existing methods in handling frequency- domain degradations, we introduce an RGB-space DCT-based filtering module for multi- fullti-freuuecyy separation and Dynamic Discrete Sequence Fusion Transformer to integrate frequency- domain priors. Experiments on real-world datasets show our method outperforms of-te-of-the-ee techniques, offering robust solution for low-light enhancement and denoising.
10

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Published as a conference paper at ICLR 2025
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13


Reviews:
[{"review_id": "y9tOJEosHO", "reviewer": "Reviewer_Dq7D", "summary": "This paper introduces an interpretable, unsupervised framework for joint denoising and low-light enhancement of images, focusing on real-world scenarios. \n\nThe method employs physical imaging principles and Retinex theory for decomposing images into illumination and reflection components.", "questions": "1. How does the proposed method handle edge cases such as extreme noise or heavy color distortions, which may not follow typical low-light degradation patterns?\n\n2. Given the success of the DCT-based frequency decomposition, have the authors considered combining this with other frequency-domain transforms (e.g., wavelets) to enhance robustness across diverse types of degradation?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper combined the Retinex model and data-driven methods since the first effort of RetinexNet. \n2. Experiments are sufficient.\n3. Frequency domain decomposition is well-used. \n4. The unsupervised low-light enhancement method is a promising direction. \n5.  The self-supervised denoising method based on neighboring pixel masking is well-aligned with the challenges of handling zero-reference images.", "weaknesses": "1. For low-light enhancement task, I want to see more results on LIME, NPE, MEF, DICM and VV. Since these datasets do not have ground truth, then is more fair to justify the effectiveness.\n\n2. Missing citations of real-world low-light enhancement methods,\n[1] Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution\n\n3. The paper lacks a discussion on the computational complexity and runtime efficiency of the proposed model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SJinHVEKqz", "reviewer": "Reviewer_SCTr", "summary": "This paper proposes an interpretable unsupervised joint denoising and enhancement method suitable for real-world low-light scenes. Based on the physical imaging principles and Retinex theory, discrete cosine transform (DCT) is used for frequency domain decomposition, and an implicitly guided hybrid representation strategy is introduced to effectively separate complex compound degradations, thereby realizing the detection and optimization of complex degradation problems caused by low-light conditions. The authors conduct experiments to verify the effectiveness of the proposed method.", "questions": "Please see the Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The authors explore the difficulties traditional methods face in dealing with complex degradation issues such as noise, brightness, and contrast, and propose solutions that significantly outperform existing technologies.\n2. Technically, the authors introduce a spatial frequency domain filtering module that uses discrete cosine transformfor explicit multi-band separation, which facilitates the decomposition of enhanced images into illumination and reflectance maps.\n3. Experiments depict the effectiveness of the proposed method on multiple datasets.", "weaknesses": "1. Some of the latest methods are not compared, such as RetinexFormer， and Rerinex-Diffusion. Besides, the experiments do not provide statistical indicators such as standard deviation or confidence interval in Table 1 and Table 2, which makes the reliability and stability of the results unclear. Moreover, the generalization ability of the proposed method is not explored, for example, when training the method in low-light environment and testing it one other exposure scenes.\n\n2.  Although authors claim the proposed method is new, quantities of methods have explored introducing frequency-based techniques into image enhancement including some components-decomposition-based methods (such as [1])， what are the special characteristics of the proposed method that introducing the techniques that used above into retinex-based mechanism?\n\n[1] Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement, ECCV 2024.\n\n3.  There are too few visual results, for example, the visualization results of more real-world scenes (other low-light datasets or other exposure scenes) can be provided. Moreover, the detailed mechanism of why the proposed method works is not presented. How it has better performance than previous methods?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WNykMvgYjM", "reviewer": "Reviewer_v7Se", "summary": "In this article, to address the complex degradation issues of images in low-light scenarios, the author proposes a training strategy based on physical imaging principles and retinal theory. Specifically, first, the author derives a novel training strategy from paired images under varying lighting conditions and noise levels; second, the author introduces an implicit guidance hybrid representation strategy through DCT transformation, effectively separating complex composite degradation; finally, the author develops a retinal decomposition network based on the implicit degradation representation mechanism. The article demonstrates the effectiveness and reliability of the proposed method through extensive experiments.", "questions": "1. On page 4, lines 175-180, the author proposes the use of Discrete Cosine Transform (DCT) to separate different frequencies of images (including chromatic, semantic information, edge contours, and noise intensity). What is the theoretical basis for this approach? Or is it more of an empirical practice? If so, would the performance of the model be affected if different methods were used to decompose the feature maps after Discrete Cosine Transform?\n2. On page 6, lines 322-333, the author presents the loss function of the model. However, during the experiments, the author seems to have not conducted an in-depth exploration between the loss function and the performance of the model. If such research were included, it would make the argument of the paper more complete.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The author proposes a novel retinal decomposition network based on an implicit degradation representation mechanism in the article, which shows certain effectiveness in image enhancement for low-light scenarios;\n2. The language of the article adheres to English writing standards and is fluent;\n3. The article provides mathematical proofs for each module of the network, making the logic of the article strong;", "weaknesses": "1. On page 4, lines 175-180, the author proposes the use of Discrete Cosine Transform (DCT) to separate different frequencies of images (including chromatic, semantic information, edge contours, and noise intensity). What is the theoretical basis for this approach? Or is it more of an empirical practice? If so, would the performance of the model be affected if different methods were used to decompose the feature maps after Discrete Cosine Transform?\n2. On page 6, lines 322-333, the author presents the loss function of the model. However, during the experiments, the author seems to have not conducted an in-depth exploration between the loss function and the performance of the model. If such research were included, it would make the argument of the paper more complete.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6oYkPWKo9p", "reviewer": "Reviewer_wCSY", "summary": "This manuscript introduces an interpretable, unsupervised framework designed to enhance and denoise real-world low-light images without the need for paired training data. The proposed method leverages physical imaging principles and retinex theory to derive a training strategy based on paired sub-images with varying illumination and noise levels. The framework employs the Discrete Cosine Transform (DCT) to perform frequency domain decomposition in the sRGB space.", "questions": "See the weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. It is technically sound to incorporate the neighbor2neighbor denoiser with the task of unsupervised LLIE \n2. The authors provided code in the supplementary materials.", "weaknesses": "1. In Equation (8), the term P1 is introduced without prior explanation, and it appears that the FIcoder is omitted in the process. Additionally, while the authors describe P1 in Equation (15), the description is still quite vague. Could the authors please clarify the shape of P1, how it is learned, what constraints are used, and whether it can be visualized?\n\n2. If Equations (9) and (13) are identical to the standard DCT/IDCT transformations, there is no need to present them in the paper.\n\n3. In Equation (14), the L_{reg} term is not explained. Could the authors please provide an explanation for this term?\n\n4. In Equation (15), what is the difference between the first and third terms? Additionally, the authors are requested to show several decomposed illumination and reflection maps to demonstrate that the constraints in this equation are sufficient to learn reasonable Retinex decomposition results.\n\n5. On Line 150, the reference year for noise2noise is incorrect; the paper was published in 2018, not 1803.\n\n6. Figures 1, 2, 3, and 5 are not explained in the main text (at least I could not find them), while Figure 4 is explained but not correctly referenced.\n\n7. In Figure 6, I do not see the advantage of the author's method over the SCI method, and the authors avoid explaining this point in the paper.\n\n8. In Figure 7, the author's method still leaves noticeable noise, which I did not see in the Neighbor2Neighbor comparative experiment. Is there an issue with the author's training process?\n\n9. The authors should introduce qualitative comparisons on an independent test set to demonstrate the superiority of their generalization performance. I personally believe this is a key experiment to distinguish whether a low-light enhancement model is valuable, and it should also be an advantage of unsupervised methods. Recommended datasets include LIME, NPE, MEF, DICM, and VV, as used in the Retinexformer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NCDS2GZ4GJ", "reviewer": "Reviewer_h1DE", "summary": "This paper develops a zero-reference joint denoising and low-light enhancement framework designed for real-world applications. \nThe framework incorporates a self-supervised optimization strategy and physical priors to effectively capture complex degradations. \nGrounded in Retinex theory, the optimization process generates paired low-light and enhanced images by dynamically adjusting brightness, thereby establishing mutual self-supervision. \nA Discrete Cosine Transform (DCT) is employed to extract degradation representations across varying levels, facilitating degradation decomposition and removal within the frequency domain.", "questions": "Please refer to Weaknesses", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper presents a zero-reference joint denoising and low-light enhancement framework that operates without the need for paired or unpaired images, a crucial feature for real-world applications.\n\n2. The framework introduces a novel neighboring pixel masking strategy and creates paired images with varying brightness levels to enable self-supervised image denoising and decomposition.\n\n3. Experiments on real-world datasets show that the proposed framework outperforms existing zero-reference low-light image enhancement methods.", "weaknesses": "1. The reviewer notes that the proposed neighboring pixel masking strategy appears to draw from methods like Neighbor2Neighbor [1] and MAE [2], which are known to sometimes produce over-smoothed results. The authors are encouraged to clarify the differences between their approach and these methods, highlighting any improvements that address over-smoothing issues.\n\n2. Conducting ablation studies on the size of the neighboring mask would be beneficial to assess its impact on preserving texture details. Such analysis could provide a clearer understanding of the mask’s effectiveness in enhancing visual quality.\n\n3. The exposure loss function relies on a pre-defined exposure level, similar to the approach in Zero-DCE. However, in scenarios with non-uniform or overexposure, the results may tend toward overexposure and color distortion. The authors are encouraged to consider integrating an adaptive exposure adjustment strategy in future work to address these limitations.\n\n4. While the authors claim that the proposed framework can both denoise and enhance images, the LOL dataset—characterized by slight noise degradation—may not fully validate its denoising capabilities. It would be beneficial to conduct experiments on low-light image enhancement datasets with significant noise levels, such as LSRW or other image denoising datasets, to substantiate the framework's performance in more challenging conditions.\n\n[1] Huang T, Li S, Jia X, et al. Neighbor2neighbor: Self-supervised denoising from single noisy images[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 14781-14790.\n\n[2] He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 16000-16009.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)