Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
Authors: Hongzhi Wang
Xiubo Liang
Jinxing Han
Weidong Geng
Venue: NeurIPS 2025 poster ()
Keywords: Spiking Neural Networks
Low-light Image Enhancement
Primary area: neuroscience_and_cognitive_science

Abstract: Low-light image enhancement (LLIE) aims to improve the visibility and quality of images captured under poor illumination. However, existing deep enhancement methods often underemphasize computational efficiency, leading to high energy and memory costs. We propose \textbf{Spike-RetinexFormer}, a novel LLIE architecture that synergistically integrates Retinex theory, spiking neural networks (SNNs) and a Transformer-based design. Leveraging sparse spike-driven computation, the model reduces theoretical compute energy and memory traffic relative to ANN counterparts. Across standard benchmarks, the method matches or surpasses strong ANNs (25.50 dB on LOL-v1; 30.37 dB on SDSD-out) with comparable parameters and lower theoretical energy. Our work pioneers the synergistic integration of SNNs into Transformer architectures for LLIE, establishing a compelling pathway toward powerful, energy-efficient low-level vision on resource-constrained platforms.

Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
Hongzhi Wang
Xiubo Liang"
School of Software Technology
School of Software Technology
Zhejiang University Ningbo, China whzzjuūzju. edu. cn
Zhejiang University Ningbo, China xiubolzju.eo edu. cn
Jinxing Han
Weidong Geng
School of Software Technology
College of Computer Science and Technology Zhejiang University Hangzhou, China gengudłzju edu, cn
Zhejiang University Ningbo, China hanjx0zju. edu. en
Abstract
Low-light image enhancement (LLIE) aims to improve the visibility and quality of images captured under poor illumination. However, existing deep enhancement methods often underemphasize computational efficiency, leading to high energy and memory costs. We propose Spike- Baakke-RetinexFormmer novel LLIE architecture that synergistically integrates Retinex theory, spiking neural networks (SNNs) and Transformer-biit design. Leveraging sparse spike-driven computation, the model reduces theoretical compute energy and memory traffic relative to ANN counterparts. Across standard benchmarks, the method matches or surpasses strong ANNs (25.50 dB on LOL-v1; 30.37 dB on SDSD-out) with comparable parameters and lower thcoretical energy. Our work pioneers the synergistic integration of SNNs into Transformer architectures for LLIE, establishing compelling pathway toward powerful, energy-efficien low-level vision on resource-consee constrrinddt platforms.
Introduction
Capturing images in low-light conditions is challenging due to limited photons, sensor noise, and constrained dynamic range. These factors produce dark, noisy images that hinder downstream tasks like detection or recognition. LLIE techniques seek to brighten such images and restore details, enabling better visual quality and utility. Early approaches relied heuristic image processing (gamma correction, histogram equalization) and the Retinex theory of human vision M. which decomposes an image into reflectance and illumination. Traditional Retinex-based algorithms [2] improved visibility by estimating a smooth illumination map and enhancing the reflectance, but often suffered from artifacts (haloing, color distortion) and were limited by manual parameter tuning.
The advent of deep learning spurred significant advances in LLIE. Convolutional neural networks (CNNs) have been trained directly map dark images to brighter ones, outperforming classical methods in handling noise and complex artifacts. Representative works include LLNet 4 one of the first deep autoencoders for natural low-light enhancement, and LightenNet (5) for weak illumination images. More specialized models integrated Retinex theory, such as RetinexNet [6] which learned decompose image into reflectance and illumination and enhance them with
Conresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Block (SIGAR)
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Target Image
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2-2 Dexoov
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Corruption Restorer
Figure 1: Architecture of the Spike- Reakee-RetnexFommer. It consists of two main stages: (1) the Spiking Illumination Estimator, which predicts illumination features and a light-up map to adjust initial brightness, (2) the Spiking Corruption Restorer, which utilizes multi-scale U-Net structure with Spiking Illuminatoon-Gudde Attention.
deep networks. Many subsequent methods built on this idea, proposing improved Retinex- based networks with attention refinement modules 7 8 99. Unsupervised and zero-reference techniques have also emerged: Zero-DCE [10 optimizes a deep curve mapping without ground truth, while EnlightenGANN [12 uses generative adversarial learning to enhance lighting without paired data. Recent trends include normalizing flow models [13] and diffusion models [14] for LLIE, and Transformer-biae based architectures [15] to capture global illumination context. Despite improved enhancement quality, most deep LLIE methods are computationally heavy and
[I6]
17,, which limits deployment
coruurce-constrainee devices.
Meanwhile, SNNs have gained interest as the third generation of neural networks, offering event- driven computation inspired by biological neurons [1]. Neurons communicate via sparse binary spikes, leading to significantly reduced power consumption on neuromorphic hardware 19]. SNNs have achieved competitive performance on classification 20 and object detection [21 tasks with far lower energy usage than standard ANN models. However, relatively few works have applied SNNs to low-level vision: LLIE is continuous most prior SNN works focused on classification or recognition with discrete labels. Directly applying SNNs to image enhancement must handle fine-grad grained color and illumination adjustments, requiring high precision despite the coarse spike- spike-based computations. [22] encoded pixel intensities into spike latencies and used recurrent SNN (with ConyLSTM) for unsupervised LLIE. This demonstrates that SNNs can gradually capture image structure via spike timing.
regression problem (enhancing pixel intensities), whereas
Transformers 23 have revolutionized many vision tasks due to their self-attention mechanism capturing long-range dependencies. Vision Transformers [24] and related models have shown excellent performance in recognition and even low-level image restoration [25] 26]. For LLLIE, Transformers can globally model illumination variation and object context, yielding more balanced enhancement [15]. Merging the energy efficiency of SNNs with the representational strength of Transformers is a promising direction that remains underexplored. Recent studies have started integrating spiking neurons into Transformer models 227
31,, mainly for classification sequential data processing. These spiking Transformers achieve comparable accuracy to standard Transformers while greatly reducing Multiply-Accumiln operations via ppike-based attention. This progress motivates our approach to design a spiking Transformer for LLIE.
28 29 30,31]
In this paper, we propose Spike- Moike-RRtinexFFomeet a spike-driven variant of RetinexxFrmmer that re-instantiates the one-stage Retinex parameterization with temporal spike coding, Leaky Integrate-and and-Firdd (LIF (32) neurons, surrogate-gradient training,
SNN architecture for LLIE, Spike- Snike--Reiinxxoomeee

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and an event-driven attention mechanism. To our knowledge, this is among the first feedforward spik- ing Transformer-stye network tailored to LLIE. By coupling Retinex- based illumination modeling with event-driven computation, the approach aims at high-fidelity enhancement under constrained compute and energy budgets. Specifically, a spiking illumination estimator predicts a light-up map and illumination features consistent with the Retinex reparameterization, while Retinex restoration module employs spiking iiriinatioo-uuidee attention (SIGA) to aggregate long-range context and suppress noise. Our network processes images over T time steps, accumulating an enhanced output from spiking neuron responses. In summary, our contributions to the community include:
We introduce Spike-Retiiee Retinex which instantiates one-stage Retinex enhancement pipeline entirely using spiking primitives and is trained with surrogate gradients, demon- strating that continuous image restoration can be effectively addressed in the spike domain.
We develop a spiking illumination-gues guidedd attention mechanism mechanism-iiplemeenne implemented via spike- coincidence affinities, illumination-gan -ated sparsification, and binary value routing and integrate it into the Retinex framework to enable long-range interactions without forming dense QK' matrices or softmax normalization.
Across standard LLIE benchmarks, Biike--ReiinexFrmee attains competitive enhancement quality relative to representative ANN counterparts while using comparable parameters and fewer theoretical FLOPs.
2 Related Works
2.1 Low-Light Image Enhancement
Early methodologies for low-light image enhancement drew upon traditional image processing techniques and models of human visual perception, prominently featuring the Retinex theory M. which models an image as a product of reflectance and illumination components. Subsequent developments, such as Multi-Scale Retinex with Color Restoration (MSRCR) 2 and LIME [3). focused on estimating illumination maps to improve image visibility; however, these approaches necessitated manual parameter tuning and were susceptible artifact generation. The emergence of deep learning marked a paradigm shift, introducing data-driven approaches that often yielded superior performance. CNN-based models, exemplified by L.LNet 4 and I. LightenNet (5). utilized large-scale datasets (LOI (6)) to learn the image enhancement mapping directly from data. While not exclusively focused on LLIE, Ignatov et al. (33] explored broader applications of CNNs in image enhancement, contributing to the foundational understanding in the field. The principles of Retinex theory were subsequently incorporated into deep learning frameworks. Notable models include RetinexNet 6 and its variants [7 8), which explicitly perform image decomposition into illumination and reflectance, with applications extending to specialized domains such as underwater imaging [9] and back-lit scene enhancement.
To address the limitations associated with paired training data, unsupervised and semi-supervised methods were developed. For instance, Zero-DCE [TO] and its subsequent refinement [T] utilized
212 and DeepExposure 34] employed generative models for unpaired learning scenarios. Yang et al. (35) proposed a semi-supervised framework designed to strike a balance between image fidelity and perceptual quality. Recent investigations have focused on leveraging sophisticated neural architectures. Models based on normalizing flows [I3 and diffusion probabilistic models [4] have demonstrated capabilities for high-fidelit image enhance- ment, albeit often at significant computational cost. Inspired by their success in other computer vision tasks such as super-resolution [25] 26). Transformer architectures and attention mechanisms have also been adapted for LLIE, as evidenced by works like [15]. In related vein. Tang et al. (36) introduced a method focusing on disentangling various image components to achieve more flexible and controllable enhancement.
non-reference loss functions, while EnlightenGANN
2.2 Spiking Neural Networks
SNNs are a class of biologically-inspire computational models that process information through dis- crete temporal events, termed spikes, contrasting with the continuous activation values characteristic of conventional Artificial Neural Networks (37) Within SNNs, individual neurons integrate input currents over time, generating output spike when their internal membrane potential surpasses
3

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predefined threshold. This event-driven operational paradigm offers the potential for substantial en- engy efficiency, as computationally expensive multiply-acce accumulatee operations prevalent ANNs are often replaced by simpler arithmetic operations (additions), and power consumption is predominantly associated with active spiking events. Maass [8] conceptualized SNNs as the third generation of neural network models, and subsequent research has established their viability as an alternative to ANNs specific application domains. For instance, [19] demonstrated that neuromorphic hardware platforms executing SNNs can achieve energy efficiencies orders of magnitude greater than those of Graphics Processing Units (GPUs) processing functionally equivalent ANN models.
The practical training of SNNs was initially impeded by the non- non-differentiable nature of the spike generation mechanism. However, methodologies such as ANN-to-SNo conversion and surrogate gradient learning have facilitated the effective training of deep SNN architectures Conseqquell,, SNNs have demonstrated competitive performance on established image classification bench- 41 42 and have been successfully applied to more complex vision tasks, including object detection (Spiking- YOLO 21) Notwithstanding these advancements, the ap- plication of SNNs to low-level vision tasks requiring continuous-valle outputs, such as image enhancement, remains relatively underexplored. A principal challenge this context the genera- tion of high-resolution, analog-like outputs (an enhanced image) from discrete spike trains. This often necessitates sophisticated encoding schemes for pixel intensities, such as rate or temporal coding, thereby introducing additional representational and computational complexity. Addressing this, [22] investigated LLIE by encoding pixel intensities into spike latencies, which were then processed by recurrent SNN. Their findings demonstrated the feasibility of image enhancement using this paradigm, achieving notable computational reductions compared to some ANN counterparts. These results provide preliminary evidence for the capability of SNNs in image enhancement tasks.
marks [38, 20 [39 40
3 Method
3.1 Spiking Retinex-
Enhancement Framework
Following Retinex theory, a low-light RGB image E [0. 1 [HHXx3 x3 is modeled the Hadamard
product of reflectance image R E [0, 1),1)xxXx²
and illumination map L E [0, 1]
= ROL
(1)
S+5 Clo
Directly obtaining a lit image by element-wise wise division is numerically fragile when L is small. In- stead, we introduce light-up map L that approxi- mates --1 and enforces the constraint L L 1. Multiplying by L yields a lit-up image
1x1 Can
Illunieation
Figure 2: Spiking illumination estimator E.
Iu = 1OL R +
where C E "xx'33 collects the overall corruption introduced by sensor noise, quantization, color shifts, and the light-up process itself. We realize (2) in one stage with spiking illumination estimator and a spiking corruption restorer. We adopt one-stage Retinex-based framework with two spike-driven modules:
(hu. Fm.) = E(I
Ien = -GJuur Fiu),
where E denotes the spiking illumination estimator and G the spiking corruption restorer. The illumination prior Lp R RHXWXX is the channel-average mean of the input image:
Lp mean(
(4)
and both E' and G are implemented with LIF spiking neurons and operate over T discrete time steps; gradients are estimated via surrogate functions.
As illustrated in Fig. 2 is a shallow spike-driven CNN that maps [ Lp] #R to a light-up image and illumination features:
Cl: 1x1 fusion
+ C2: depth-wise 5x5 (light-up features)
+ C3: 1x projection.

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G' is U-Net style encoder-decoder with skip connections, implemented entirely with spiking layers. At each decoder stage, we insert a SIGA block that consumes the current feature maps together with the scale-aligned Fm, (from (3), injecting long-range, lighting-aware context without dense softmax attention. lightweight spike-driven head predicts residual A/ that
Ien
= Iu + AT.
(5)
The whole pipeline trained end-to-end with surrogate gradients.
3.2
Spiking Neuron Model and Training
We use LIF neurons with soft reset and a single, shared time budget T steps for both training and inference. For each layer l and time step t E (.,...., T),
[ = ( [t- [1)
(( = (1 (v))) HO - = U00 Vil. 5(0)
with (0)) 0, leak ( E (0, 1). and a fixed threshold v
Input encoding and T I-mormaalzation.
The illumination estimator E' (see (3) is executed first and receives a time-shared, T-normalized current
6 [t]
T Pin I, Lp),
Pin [X, Y) ns (xX) || Kip o(Y),
((2) log(1 1 + +2),
where denotes channel concatenation. After E predicts the light-up map L and features Fiu (cf. 3 we form 4u = I @ L (cf. 22) and its prior L mean( (u).. The decoder G then uses
T Pinl Jmu L,",,
(8)
which keeps the total injected charge approximately invariant T. We use (-6 and (K, (p,)p)111.0, 0.5) in all experiments.
Illumination-guidee FiLM. For f2 in the decoder G, the synaptic current is a convolution on spikes modulated by per-channel FiLM parameters:
7 ( @ (W s(t- + g(5)
where is a standard convolution and (9)) glt) CRC, are time-shared (constant over t) and broadcast spatially. They are predicted once from temporally aggregated illumination features (from 33)
T
Fin = T
Σ
Fult GAP(TT..
[a0013001 w( + 69
(10)
( amin + (Amax - Omia) (a))),
8 = tanh(
(11)
with (Amin, Olmax) (0.5, 2.0), = 0.05, and (-) the logistic sigmoid. When Fi is lower- resolution, we bilinearly upsample inside the FiLM head; BatchNorm (if used) in eval mode with EMA statistics to remain stable under sparse spikes.
Surrogate gradients and readout. We train end-to-end with simple piecewise- linear surrogate derivative around the firing threshold. The network uses single-step readout identical in training and inference:
Y = c 4n + Head (ou 0, 1),
(12)
where Head is 1x1 convolution to channels; gradients through clip use ttraight-through estimator on [0, 1].
A slow, causal threshold adaptation was explored for extremely dark scenes but is disabled by default to keep latency minimal.
5

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Objective. The loss combines an L1 image term, a firing rate regularizer, and temporal TV on the pre-readout state to suppress flicker without svor-smog spatial details:
|| J1l1 Aapk AvgRate(S) Avv TV, (V((Hoou)))::))))
(13)
with (Aspk: Atv) (10-+, 5x10-*).
3.3 Illumination-Guice Spiking Transformer
We build on the U-Nesstyle backbone summarized Sec. 3.1 and Fig. In this subsection we focus on how illumination guidance is injected the decoder scales: the scale-aligned illumination features Fin condition both the attention (via SIGA) and FiLM modulation.
At each decoder scale, we deploy a SIGA module as the core long-range dependency unit. SIGA operates on spike trains with multi-head processing: query-key spike coincidences open hard binary gates that route value spikes, while per-head FiLM parameters predicted from scale-aligned illumination
the Q/K/V projections and gating thresholds and are shared over time. All attention computations are realized by LIF neurons unrolled across T steps; BatchNorm layers (when present) run evaluation mode with EMA statistics to maintain stable activations under spike sparsity. The feed-forward branch is a two-layer spiking MLP with LIF activations, forming a standard attention+FFN block in spiking form and serving as the decoder's illumination-awar context aggregator.
SIGA mechanism: Each SIGA takes spike-encoded query, key, and value streams (Q(4), [[d, V(() and scale-aligned illumination feature Fiu- Multi-head processing is used: channels are split into H heads, computed in parallel, and concatenated. For each head, Flu drives FiLM-style parameters that modulate the Q/ K/ projections and set illumination-awee aware gating thresholds; these parameters are predicted once and shared across time steps. Spike coincidences between Q and K open binary gates that route V spikes, yielding an event-driven hard-attention pattern implemented by LIF neurons unrolled over steps.
Jincr
2x: [0.1]
NXC
[0.
N
K, [0.1]
NXC
Illumiantion features
Input X
Linear
F [0
N
C
Figure 3: Schematic of the SIGA module: illustrating spike-based Q, K, V interactions, modulated by the illumination features (from L/F(u), to generate binary attention mask for value routing.
To compute attention in SIGA, we forego explicit floating-poin matrix multiplication and softmax. Instead, spike-driven interactions implement hard gating: when a query neuron at position fires at time t, synapses to candidate keys are activated; if key neuron at position j simultancously fires, the coincidence [+[t]=1A A [t] opens an instantancous hard gate Aiş [t] whose threshold is modulated by illumination features. Routing uses the current A, [t] (preserving causality), while optional co-firing accumulators over t=1...7 .T' integrate statistics. Owing to spike sparsity (and optionally a local neighborhood), each query selects small subset of keys, enforcing efficient hard attention without forming dense QK complexity scales with spike rate and neighborhood size rather than quadratic token pairs.
Given the gates at time I, value spikes are routed accordingly: a value spike [1[t] contributes to query position iff Atg [t] is open. Implementationallly each head employs LIF neurons receiving value

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Input
UR Retinex
EnlightenGAN
Diff-Retinex
RetinexFormer
Ours
Target
Figure Low-light image enhancement results on the LOL-v1 dataset. Compared methods: URetinex-Net. EnlightenGAN, Diff- Reff-Retinex, RetinexFormer and Spike- Spike-Retinexxormeeee RetinexFFomer(Ouss)
inputs masked by Ary [t:: absent a gate, [t[t] has no effect i. The SIGA head output at position i a spike train emitted by a LIF unit that fires when its integrated (binary-weighted input crosses threshold; illumination features can shift this threshold via FiLMMsttyle bias.
After the attention stage, head outputs are concatenated and passed through spiking feed-forwart module (two layers of linear spikes with an intermediate LIF activation), analogous to Transformer FFN. Residual connections wrap the SIGA+FFN block with proper temporal alignment-residuaai domain at each time step-so that the training-inference readout remains identical to the rest of the network. Multiple SIGA blocks are placed across decoder stages; at each scale, illumination-guit -uided spiking attention supplies long-range, lighting- -wware context while preserving wvent-drienn efficiency.
are added in the membrane-potentte
Experiments
4.1 Experimental Setup
We evaluate Spike- Ferkee--eenneeormerr on a comprehensive suite of standard benchmarks for LLIE, largely following the protocol of [43]. These include: LOL-v1 (6). LOL-v2 (real and synthetic) 44
SID [45]. SMID [46]. SDSD (indoor and outdoor) 477). MIT-Adobe FiveK [48), and LIME 49
which commonly used for qualitative evaluation. For datasets with paired ground truth, train separate model per dataset using the official or widely adopted splits; for RAW datasets, we convert to sRGB via the standard ISP pipeline before computing losses and metrics to ensure comparability.
All experiments share the same backbone and training hyperparameters as Sec. 3 Unless otherwise stated, we use the AdamW optimizer (base learning rate 2 x 10 10-4) with cosine annealing (optional warm-up), unrolling the spiking dynamics for T time steps and applying global- clipping at 1.0. Each model is trained until the validation performance saturates. We report the Peak Signal-to- -onal- Ratio (PSNR) and Structural Similarity (SSIM) (50) on sRGB results for quantitative comparison. For model complexity, provide parameter counts (Millions) and FLOPs at 256 x 256 input resolution.
4.2 Comparison with ANN models
We compare Dpike-RetinexFormee with recent ANN methods, including fetinex-basedd CNNs and Transformer-based models: RetinexNet [6]. KinD [5I] DeepUPE [52]. RUAS [53], MIRNet [54].
Uformer [55], Restormer (26), SNR-Net (56), and RetinexFormer 43 Quantitative results on
benchmarks with ground truth are summarized in Tab. (LOL) and Tab. (SID, SMID, SDSD).
Overall, Spike-Retinex delivers competitive performance across most datasets and often matches strong ANN baselines. Notably, on the widely used LOL-v1 dataset, our method attains
7

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Table 1: Quantitative comparison on the datasets of LOL-v1, LOL-v2 (real and synthetic).
Method
FLOPs
Params (M)
LOL-v1 PSNR/SSIM
LOL-v2-R PSNR/SSIM
LOL-v2-S PSNR/SSIM
(G)
RetinexNet 6
587.5
0.84
16.77/0.560
15.47/0.567
17.13/0.798
DPepUPR 52
21.1
1.02
14.38/0.446
13.27/0.452
15.08/0.623
KinD [1]
35.0
8.02
20.86/0.790
14.74/0.641
13.29/0.578
RUAS [53
0.8
0.003
18.23/0.720
18.37/0.723
16.55/0.652
MIRNet [54
785.0
31.60
24.14/0.830
20.02/0.820
21.4/0.876
Restormer 26
144.3
26.10
22.43/0.822
19.94/0.821
21.41/0.830
SNR-Net
[56
26.3
4.01
24.61/0.845
21/48/0.44
24.14/0.928
Retinex Former
43
15.6
1.61
25.16/0.845
22.80/0.840
25.67/0.930
Ours
16.2
1.50
25.50/0.842
23.38/0.848
26.47/0.938
Table 2: Quantitative comparison SID, SMID, and SDSD (indoor/outdoor) datasets. Our method
achieves top performance across these diverse henchmarks.
Method
SID PSNR/SSIM
SMID PSNR/SSIM
SDSD-in PSNR/SSIM
SDSD-out PSNR/SSIM
FLOPs
Params
(G)
(M)
KinD
18.02/0.583
22.18/0.634
21.95/0.672
21.97/0.654
35.0
8.02
MIRNet
54
20.84/0.605
25.66/0.762
24.38/0.864
27.13/0.837
785.0
31.60
Uformer
55
18.54/0.577
27.20/0.792
23.17/0.851 859
23.85/0.748
12.0
5.29
Restormer 26
22.27/0.649
26.97/0.758
25.67/0.827
24.79/0.802
144.3
26.10
SNR-Net [56
22.87/0.625
28.49/0.805
29.44/0.894
28.66/0.88 66/0.66666
26.3
4.01
RetinexFormer
24.44/0.680
29.15/0.815
29.77/0.896
87/0.77777
15.6
1.61
Ours
24.68/0.681
29.43/0.820
30.45/0.903
30.37/0.885
16.2
1.50
25.5 dB PSNR and 0.842 SSIM, which on par with Retinex (25.2 dB, 0.845) and clearly ahead of carlier Retinex-based CNNs such as RetinexNet (16.8 dB) and KinD (20.8 dB). On the more challenging LOL-v2 (synthetic), Sppike-RetinexFae gain over RetinexFormer with comparable SSIM; trends SID/SMID/SDSD are similar. We attribute this competitiveness to the synergy between Retinex decomposition and spiking neural dynamics: Illumination-guida guided spiking attention helps balance contrast, while iterative spike integration aids noise suppression under extreme low light. In terms of perceptual quality, our results typically exhibit fewer color shifts and artifacts; shown in Fig. 4 method yields natural, well-exposed images with preserved details, whereas some baselines may over-smooth or leave residual noise.
reaches 26.5 dB PSNR, representing 0.8
As seen in Tab. D andd our spiking approach shows competitive improved performance across the LOL variants and the extremely dark, noisy datasets (SID, SMID, SDSD). On average, Spike- RetinexFormer improves PSNR by ~0. dB over strong prior methods, with gains up to ~ 0.8 dB depending on the benchmark. In particular, for the dark indoor scenes of SDSD, our method recovers additional details, reaching 30.45 dB. Meanwhile, the model is compact-1.5M parameters and lower than heavy Transformers such as Restormer (26M, 144G). We
16.2 G EELOPs-noticann attribute this efficiency to the one-stage design and the sparsity of spiking computation (neurons do not fire at every time step), making the approach practical for deployment. On neuromorphic hardware, energy consumption is expected decrease because operations rather than dense activations. For example, a 32-bit MAC costs an order magnitude more energy than accumulate (AC) [9]. and only ~18% of neurons fire per time step in network (Tab. 5). back-of-the-envelln estimate suggests on-the-order-of 5x-10x energy reduction versus comparable ANN models under these assumptions. While these savings are theoretical (we have not measured power on neuromorphic chip), they highlight the potential advantage of event-driven processing for power efficiency.
triggered by spikes
4.3 Ablation Studies
We systematically ablate the one-stage Retinex formulation (ORF), the proposed SIGA, and the influence of the time step T. All ablations are conducted on LOL-vI using single RTX 3090.

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Table 3: Backbone ablations on LOL-vl (full-resolution).
Variant
PSNR
SSIM
Params (M)
FL.OPs (G)
Baseline (Spike-U-Neg
23.11
0.789
1.20
9.5
+ ORF
24.56
0.822
1.45
11.8
ORF + W-MSA
25.07
0.835
1.62
13.2
ORF G-MSA
25.29
0.837
1.75
16.8
ORF SIGA (ours)
25.50
0.842
1.68
14.0
Table 4: Ablation on neuron type and architecture (LOL-vI, 7-8).
Activation
Architecture
PSNR
SSIM
Energy (mJJ
ReLU
RetmeexFormer
25.16
0.845
71.6
LIF
RetinexFormer
20.31
0.714
7.5
LIF
Brike-ReinnxFoomerr
25.50
0.842
16.7
IF
Spike-Retinex Formnee
25.31
0.831
18.9
PLIF
Spike-Ree Fikke-RetinexFomerr
25.49
0.833
15,8
CLIF
58
Spike-RetinexFomee
25.61
0.848
16.3
Tab. 3 compares five progressively enhanced variants. Baseline is pure spiking U-Net (no illumina- tion branch, SIGA). Introducing the illumination estimator and light-up operation (+ ORF) yields a a+1.45 dB PSNR gain 123.11-24.561 indicating that explicit exposure modeling is critical in the spiking regime. Augmenting the ORF backbone with either local-window MSA W-MSA) or global MSA (+ G-MSA) brings additional improvements. Our SIGA attains the highest fidelity, outperforming W-MSA and G-MSA by +0.43 and +0.21 dB. Qualitatively, the advantage is most evident in extreme shadows and mixed-lighting regions, where illumination-avee gating improves detail recovery and noise suppression.
We ablate neuron types on LOL-v1 (T=8) with: (i) ANN RetinexFormer; (ii) RetinexFormer with LIF (ReLUU>LIF); (iii) Spike-Retinex RetieeRReineeerreerr (LIF+ORF+SIGA) and (iv) the same spiking backbone with IF/PLIF/CLIF (Tab. 4. Swapping ReLUU~LIF in the ANN cuts compute energy to 7.5 mJ (10.5% of baseline) but reduces fidelity (PSNR 4.88 dB; SSIM 0.845 0.714). Adding ORF+SIGA in the full spiking model restores accuracy while keeping energy low: PSNR 25.50 dB (+0.34 dB vs ANN), SSIM x ANN, and 16.7 mJ (down from 71.6 -77%). Fixing the architecture, neuron choice yields small but consistent gaps: CLIF is best (25.61/0.845 /488) at energy close to LIF; IF/PLIF are slightly lower, 15.8 18.9 m.J.
Tab. repports performance and efficiency under varying time-step configurations. Using fewer steps (T=4) provides lim- ited temporal evidence, leading to mod- est PSNR drop and a higher average spike firing rate (SFR). Increasing to T-8 achieves a favorable accuracy-latency balance; pushing T-=12 offers only marginal PSNR gains while increasing wall-time nearly linearly. We therefore adopt 7-8 as the default trade-off.
Table 5: Impact of time steps T. Avg. SFR the per- centage of active neurons per step; latency is wall-time on RTX 3090 (ms); Annerg is normalized to T=8.
T
PSNR
SSIM
Avg. SFR
Latency
AEnergy
4
25,21
0.836
0.238
16.5
0.55
8
25.50
0.842
0.187
31.0
1.00x
12
25.59
0.841
0.185
46.2
1.32x
5 Limitations and Future Work
srrdware-validateer elficiency and scalability. Although SIGA avoids dense softmax attention, computing spike co- co-firing statistics can still scale as ((22) in the number of spatial tokens N when firing rates increase or sparsity collapses high- sighillluminaiin regions. To address this, we will design an event-driven sparse attention kemnel that constructs co-firing pairs from time-bucketed inverted indices, enumerating only observed spike events and thereby avoiding any dense map materialization. In addition, we will investigate top-k gating, block/tile-leeel sparsity, and kernel-level fusion of Mit-Ms illumination modulation reduce compute, memory traffic, and latency, and will release kernels and power traces to facilitate reproducibility.

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Robustness and cross-domain generalization. We will pursue RAW-award training via a differ- entiable imaging pipeline and camera-conditional normalization to account for sensor variability; cross-dataset adaptation with self-supervised consistency constraints across RAW-sRGB pairs; and uncertainty-awas exposure control that disentangles aleatoric and epistemic components to mitigate over- and under-enhancementt We also plan to extend the framework to video using temporal spike- consistency losses and frame-adaptive time steps, and to broaden evaluation with no-reference and perceptual metrics (NIQE, BRISQUE, LPIPS) alongside stress tests for flicker, haloing, and color fidelity to more rigorously assess deployment robustness.
Conclusion
We introduced Spike-Retinex low-light image enhancer that unifies spiking neural networks with Retinex-inspired Transformer architecture. By guiding spiking self-attention with an estimated illumination map, approach achieves competitive enhancement quality challenging dark images while demonstrating promising energy efficiency and compact memory use via event-driven, sparse computation. By integrating the computational efficiency of SNNs with the representational strengths of Transformers for low-light enhancement, Spike-Retinex RetinexFormet offers a practical blueprint for high-perffrmmncc,, energy frurgll vision systems on power-constie nower-constrained platforms and points toward varddare-validateed evaluation and video extensions.
7
Acknowledgement
This work partly supported by Ningbo Youth Science and Technology Innovation Leading Talent Project (2024QL044) and Ningbo Key R&D Program (2025Z047).
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Reviews:
[{"review_id": "Q6I50Nc4CK", "reviewer": "Reviewer_Phv2", "summary": "This paper introduces a new low-light image enhancement (LLIE) technique, named Spike-RetinexFormer, integrating spiking neural networks (SNNs) into a Retinex-based transformer framework (Retinexformer). By combining the energy-efficiency of SNNs and the enhancement performance of Retinexformer, the proposed method demonstrates its comparable or superior PSNR and SSIM performance compared to existing LLIE methods, while maintaining a compact model size and computational complexity.", "questions": "1. Please clearly clarify the unique contributions of Spike-RetinexFormer in Section 3, separating them from RetinexFormer baseline and SNN preliminaries.\n2. Provide qualitative visual results on the SID, SMID, and SDSD datasets to ensure a comprehensive evaluation against existing LLIE methods.\n3. Discuss the relative contribution of integrating SNN to Retinex-based LLIE framework in achieving performance improvements. Would user study or downstream task evaluation further validate the advantage of the proposed method versus Retinexformer baseline?", "limitations": "Yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 1, "strengths_and_weaknesses": "Strengths:\n+ The proposed method Spike-RetinexFormer achieves improvements in PSNR and SSIM on most of the benchmark datasets compared to existing LLIE methods, especially outperforming the Retinexformer baseline.\n+ The proposed method maintains a small model size and FLOPs.\n\nWeaknesses:\n- Section 3 intermixes preliminaries on Retinex-based LLIE framework and SNN techniques with the authors’ proposed contributions, making it difficult to follow.\n- In Table 1, the usage of bold font is not explained. A footnote clarifying which metrics are highlighted, especially in the FLOPs and Params columns, is necessary.\n- The RetinexFormer outputs shown in Figure 3 differ noticeably (e.g., color shifts) from those in the original paper [43]. The authors should clarify any differences in processing or rendering.\n- Qualitative evaluations on the SID, SMID, and SDSD low-light datasets are absent, limiting the thoroughness of the comparative analysis.\n- Many of the efficiency and quality gains appear attributable to the RetinexFormer backbone rather than the SNN integration, which diminishes the advantages of ANN-to-SNN transformation.\n- A few typographical errors remain (e.g., L42 and L66), suggesting additional proofreading.", "was_revised": true, "final_justification": "Based on the rebuttal and discussion, I slightly upgrade my score, but still on the fence. I am OK if this paper gets accepted but still think  the motivation of this work is not strongly supported.", "rebuttal": "### Q1: Clarity of Section 3 (RetinexFormer vs. SNN vs. our contributions)\n\nWe acknowledge that Section 3 in the original submission intermixes background and novel ideas. In the revised version, we will restructure Section 3 to clearly delineate the components. Section 3.1 will recap the RetinexFormer baseline and the one-stage Retinex-based framework (following the formulation from [43]), Section 3.2 will cover essential SNN preliminaries (LIF neurons, surrogate gradients, etc.), and Section 3.3 will detail our novel contributions in Spike-RetinexFormer. This includes the introduction of SpikingConvBlocks, the Spiking Illumination-Guided Attention (SIGA) module, and the integration of SNNs into the RetinexFormer architecture. By separating the RetinexFormer baseline and SNN background from our contributions, we will make it explicit which elements are prior work and which are introduced in Spike-RetinexFormer. This clarification will address the reviewer’s concern and make Section 3 easier to follow.\n\n### Q2: Qualitative results on SID, SMID, and SDSD\n\nWe apologize for this omission and will add qualitative results in a revised appendix, space permitting. We plan to include example enhanced images from Spike-RetinexFormer versus other methods on SID, SMID, and SDSD (indoor/outdoor) to complement the quantitative results (Table 2). These visual comparisons will highlight how our method handles the extreme noise and darkness in these datasets. As the paper has space constraints, we will provide these results in the supplementary material and clearly reference them to ensure a more comprehensive evaluation, as suggested.\n\n### Q3: Contribution of SNN integration vs. RetinexFormer baseline\n\nWe appreciate the reviewer’s concern that many gains might stem from the RetinexFormer backbone rather than the SNN component. Our results do indicate that the Retinex-based one-stage decomposition (the “ORF” from RetinexFormer) provides a strong foundation. However, the SNN integration *does* add value in terms of theoretical efficiency and noise suppression. In our ablation (Table 3), replacing standard attention with our spiking attention (SIGA) yielded an extra +0.21–0.43 dB PSNR improvement over non-spiking attention variants, confirming a performance benefit due to the spiking mechanism (because the spike-based attention effectively gates features under different illumination). \n\nMore importantly, the SNN brings energy efficiency: many neurons do not fire, so a lot of computations are skipped, which would not be the case in an ANN. We will emphasize in the revision that on neuromorphic hardware the energy savings are significant, operations occur only on spike events rather than every pixel. As shown in table 4, using a shorter spike time window (T=4) reduced the simulated energy consumption to ~55% (ΔEnergy = 0.55×) of the baseline setting. These efficiency gains are a direct advantage of SNN integration. \n\nWe agree that downstream task evaluation (feeding enhanced images into a detector, for example) could further demonstrate the practical benefit of our approach beyond PSNR/SSIM. Due to time constraints, we did not conduct a user study for this submission. However, we will note this as an avenue for future work. If the paper is accepted, we plan to include in a camera-ready version a brief discussion of how spike-based processing might better preserve perceptual quality (possibly referencing fewer flickering artifacts or more natural brightness adaptation, which could be validated by human viewers). \n\nWe will also clarify that the primary contribution of Spike-RetinexFormer is the novel integration of SNNs into a RetinexFormer-like architecture, yielding comparable or better results while offering neuromorphic efficiency. In summary, we will strengthen the discussion of why SNNs add value (energy and potential robustness benefits) beyond the inherited RetinexFormer baseline.\n\n### Q4: Formatting issues\n\nWe will fix these issues in the revised manuscript. In Table 1, we had boldfaced certain figures (best metrics) without an explanation. We will add a footnote or caption clarification explaining the bold font (bold indicates the best result in each column). We will also thoroughly proofread the paper to correct the noted typographical errors (the errors at original Lines 42 and 66 and any others we find), ensuring a polished presentation is important. We appreciate these pointers, and these minor fixes will be made for the camera-ready version.\n\n### Q5: Figure 3 output differences (RetinexFormer color shifts)\n\nThe reviewer observed that the RetinexFormer results we showed in Fig. 3 have noticeable color differences compared to those in the original RetinexFormer paper. Our intention was to regenerate RetinexFormer’s outputs under our evaluation setting for a fair comparison. The differences likely arise from **implementation or processing details**: we used the official RetinexFormer code and applied the same processing across methods (without additional color correction). **It’s possible that the original paper’s figure applied certain post-processing or had a different pipeline for visualization, resulting in slightly different hues.** In the rebuttal and revised paper, we will clarify this point explicitly. We will state that we reran RetinexFormer on our test images and that minor color discrepancies versus the original paper’s published images may be due to differences in color space handling. To ensure transparency, we will double-check our RetinexFormer implementation against the official one. If we find any unintentional deviations (e.g., missing a specific calibration step), we will correct them in the final submission. Regardless, we will make sure the figure caption notes that our RetinexFormer results are obtained by us (and thus directly comparable to our method’s outputs). This clarification will resolve any confusion regarding Figure 3."}, {"review_id": "kLo9AwHLZc", "reviewer": "Reviewer_da2C", "summary": "The paper synergistically combines the classic Retinex theory of image decomposition with the efficiency of SNNs, embedding these into a Transformer-like design. This is the first LLIE model built on SNNs, demonstrating that neuromorphic processors can tackle continuous-tone image restoration tasks efficiently, pointing toward energy-efficient low-level vision on resource-constrained platforms.", "questions": "1. How did you ensure high-precision output intensities from a spiking system? With only T time steps, a neuron’s spike count can represent at most T+1 levels. Was T large enough (or combined with analog membrane readings) to avoid visible quantization in the enhanced images?\n2. Do you directly use the final membrane potentials as pixel values (for finer resolution) or strictly count spikes? \n3. The success on LLIE is exciting, do you envision using a similar SNN architecture for, say, denoising, super-resolution, or event-based image reconstruction?", "limitations": "The authors should expand their discussion of technical limitations, especially regarding hardware deployment and precision.A frank appraisal of these issues would not diminish the work; instead, it would guide future efforts and underline how this is an early step toward neuromorphic vision systems. Likewise, stating any current limitations (perhaps the need for off-chip accumulation of the output image, etc.) and how they plan to address them would strengthen the paper. That said, the authors did a good job in identifying the gap in literature and tackling it; I believe they will address the above points as the work matures.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. By demonstrating that even fine-grained regression of pixel intensities can be done with spikes, this paper opens a new direction by applying SNNs to a continuous-valued image enhancement problem. \n2. The authors tailor the Q-K attention for spikes (somewhat analogous to ideas in QKFormer’s binary Q-K attention for SNNs) and use a hierarchical Retinex decomposition. \n3. The writing is clear and well-organized, making it easy to follow the technical content. Important implementation details (like using Leaky Integrate-and-Fire neurons, time-step settings) seem to be provided, which aids reproducibility. \n\n\n\n### Weaknesses\n\n1.  We don’t know if the authors had to use a smaller image size or fewer time steps to avoid simulation slowdowns. Thus, while the concept is scalable, the actual throughput on hardware for, say, real-time video enhancement is in question. A discussion of these issues is largely missing.\n2.  Producing high-fidelity, continuous-tone images with spiking neurons is challenging. The paper does acknowledge this challenge (fine-grained color adjustment requires high precision despite binary spikes), but it’s not entirely clear how it’s overcome. \n3.  There is a possible concern that using too few spikes could introduce banding or limit the enhancement quality, whereas using many spikes reduces the efficiency. The paper does not detail if any special readout mechanism (like analog membrane integration or a larger T) is used to ensure 8-bit or higher precision per pixel.", "was_revised": true, "final_justification": "First, I already believed this work met the acceptance criteria. During the rebuttal phase, the authors addressed every concern I had raised, so I raised my score accordingly.", "rebuttal": "### Q1: High-precision output with binary spikes and limited timesteps\n\nOur approach to ensure high precision is two-fold: (1) we choose a moderate simulation length T such that the dynamic range of spike counts is not the only source of precision, and (2) we leverage the analog membrane potential of the output neurons. In our implementation, after T time steps, we do not rely solely on spike counts. Instead, we take the final membrane potential values of the output neurons as the predicted pixel intensities. This means each output neuron integrates input over time and the final analog voltage (which can have many continuous levels) represents the pixel intensity. By accumulating charge over T steps, the neuron can distinguish more than T levels; effectively, the membrane potential provides a finer granularity (similar to using spike rates or weighted spikes to encode intensity). We found that T = 8 was sufficient to avoid any visible banding or quantization artifacts in the enhanced images. Empirically, even T=4 yielded reasonable quality (within ~0.3 dB PSNR of T=8) but T=8 gave a further boost with diminishing returns beyond that. \n\nWe will clarify in the paper that the final output is read out as analog values (membrane potentials), which allows the network to produce higher precision outputs despite using binary spike events internally. This detail was hinted at but not explicitly emphasized in the original text, and we will make it clear to address the reviewer’s concern about precision.\n\n### Q2: Output readout: final membrane potentials vs. spike counts\n\nAs mentioned above, we indeed use the final membrane potentials rather than just spike count to determine pixel values. Counting spikes alone (especially with small T) could limit precision. In our model, each LIF neuron in the final layer integrates input currents over the T steps. At the end of inference, each neuron has an internal membrane voltage that may not have triggered a spike at the last moment but carries fractional information. We use that analog value as the output (after appropriate scaling). This approach helped us avoid any visible quantization in practice, and we will emphasize this design choice in the revision.\n\n### Q3: Generality to other vision tasks \n\nWe appreciate the reviewer’s enthusiasm about extending this approach to tasks like denoising, super-resolution, or event-based reconstruction. We agree that our spiking RetinexFormer framework is not limited to low-light enhancement. In principle, any continuous image-to-image mapping task could benefit from the neuromorphic efficiency if the architecture is adjusted appropriately. We envision applying similar SNN-based designs to tasks such as denoising (replacing the Retinex illumination estimation with, say, a noise estimation module, and then using a spiking U-Net for noise removal) or super-resolution (where the challenge would be to produce high-res outputs from low-res inputs – spikes could be used to gradually refine the image over time). The temporal coding approach could also be beneficial in event-based imaging or HDR reconstruction, where input arrives over time. While these extensions are outside the scope of the current paper, we will add a brief discussion in the conclusion or future work section noting that this is a first step toward neuromorphic low-level vision, and that the proposed Spike-RetinexFormer architecture could be adapted to other tasks."}, {"review_id": "CZNCMny4Nn", "reviewer": "Reviewer_uAbv", "summary": "Spike-RetinexFormer is essentially a spike-driven variant of the RetinexFormer (a transformer-based Retinex model), reworked to use temporal spike coding, Leaky Integrate-and-Fire neurons, and a spiking linear-attention mechanism.  According to the authors, this is the first SNN for continuous LLIE.  In extensive experiments, Spike-RetinexFormer achieves SOTA results, improving PSNR by 0.5–1.5 dB over prior best methods.  Crucially, these gains come with drastically reduced theoretical operation counts and power usage compared to ANN-based methods.  In summary, the paper’s main contribution is demonstrating that a carefully designed spiking transformer, combining Retinex theory and sparse spike computation, which can match or exceed the performance of conventional LLIE models while offering neuromorphic efficiency.", "questions": "1. Have you tested or estimated the model on an actual neuromorphic platform? Can you quantify the energy and latency on hardware (e.g. Loihi or FPGA) as opposed to theoretical FLOPs?\n\n2.  What is the number of time steps T used during inference? How does performance trade off with fewer/more time steps (i.e. quality vs speed)?\n3. How was the network trained? Did you use surrogate gradients, learnable initial potentials, or any other methods?", "limitations": "The method is specialized to LLIE and may not directly extend to other tasks without redesign. It also assumes input can be efficiently converted to spikes; without neuromorphic hardware, the wall-clock speed may be slow. The paper reports gains mainly in PSNR/SSIM; additional user studies or real-world tests would be needed to confirm visual improvement in practice.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. Spike-RetinexFormer applies an SNN to the LLIE problem, expanding the scope of SNN applications.  This is a clear innovation: it “demonstrates that neuromorphic neural processors can tackle continuous image restoration tasks efficiently”.\n\n2. Despite using only binary spikes, the model attains *on par or better* quality than state-of-the-art LLIE methods, which validates that the spiking Retinex framework is competitive.\n\n3. By leveraging sparse spike operations, the approach markedly reduces theoretical compute cost. The paper claims “drastically reducing theoretical operation counts and energy usage” compared to dense ANNs. \n\n4. The authors compare against a wide range of baselines across multiple datasets.  and the ablation and analysis appear sufficient to support the claims.\n\n### Weaknesses\n\n1. The paper emphasizes reduced energy usage but does not report any measurements on real neuromorphic hardware or actual power consumption.  The claims of efficiency are based on “theoretical operation counts”.  In contrast, some prior SNN works have demonstrated hardware metrics (the Slack-Free SNN for HMVC[1] was tested on neuromorphic chips).  \n2. The manuscript does not fully address SNN training challenges.  There is no discussion of how surrogate gradients, initialization, or learning tricks were used.  The absence of such details makes it harder to assess reproducibility and the potential need for those advanced training methods.\n\n3. The spiking transformer processes the image over T  time steps to accumulate the output.  The paper does not report the value of T or the resulting inference latency.  Even though the FLOPs are similar to the ANN baseline, the actual runtime on conventional hardware could be much higher due to the time dimension.  \n\n[1]Nguyen, Tam, Anh-Dzung Doan, and Tat-Jun Chin. \"Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover.\" *Advances in Neural Information Processing Systems* 37 (2024): 66410-66430.", "was_revised": true, "final_justification": "My question has been solved, so I am willing to increase the score.", "rebuttal": "### Q1: Theoretical efficiency\n\nWe acknowledge this limitation. In the revised paper, we will make it clear that our power/energy claims are **analytical/indirect**. For instance, in Section 4.2 we will state that we report energy in terms of “theoretical operation count or relative GPU energy draw” as a proxy, since we did not have access to a true neuromorphic platform for measurement. We will also reference prior SNN works that have measured hardware power to give credence that SNNs *can* achieve orders-of-magnitude energy gains in practice. We remain confident that our approach would yield significant energy savings on neuromorphic devices (due to sparse event-driven processing), and we will state that hypothesis, but also be upfront that it’s not yet empirically confirmed in this paper.\n\n### Q2: Inference latency\n\nWe realize the paper did not prominently mention the number of time steps used in final inference, nor the resulting latency, which are important for practical considerations. In all our main results, we used T = 8 time steps. This choice was based on our ablation study (Table 4), which showed that going from T=4 to T=8 gave a notable PSNR improvement (+0.29 dB on LOL) while going beyond T=8 to T=12 yielded only +0.09 dB with a substantial increase in latency. Thus, T=8 was the sweet spot balancing quality and speed (as we will clarify, it provides ~1.0× relative energy baseline and good accuracy). \n\n\n\n### Q3: Training methodology\n\nWe acknowledge that we did not elaborate on SNN training tricks in the paper. For completeness, we will add a few sentences in Section 3.2 or the Experiment Setup about how we trained the spiking network. We used standard surrogate gradient descent for the non-differentiable spike function. In particular, we adopted a commonly used approach where the Heaviside step function is replaced with a smooth proxy during backprop. The revised paper will contain a short paragraph on Training Details, confirming the use of surrogate gradients and describing our training settings. This should assure readers that the network is trainable with known techniques and is reproducible."}, {"review_id": "rgJ3PCC0DX", "reviewer": "Reviewer_vYvh", "summary": "This paper presents Spike-RetinexFormer, a novel low-light image enhancement (LLIE) model that integrates spiking neural networks (SNNs) with Retinex theory.\nBy exploiting the inherent sparsity of spike-based computations in SNNs, the proposed model achieves both reduced power consumption and enhanced performance on multiple low-light benchmark datasets.\nThe architecture incorporates a spiking illumination estimation module for Retinex decomposition and a spiking Retinex restoration module, enabling effective noise suppression and contrast enhancement.\nExperimental results demonstrate that Spike-RetinexFormer outperforms existing state-of-the-art artificial neural network (ANN)-based methods in both accuracy and computational efficiency.", "questions": "Please refer to Weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**[Strengths]**\n- The authors propose a novel architecture for the low-light image enhancement (LLIE) task by integrating spiking neural networks (SNNs) with Retinex theory.\n- To this end, they incorporate SpikingConvBlock and introduce a novel Spiking Illumination-Guided Attention (SIGA) module.\n- The proposed Spike-RetinexFormer achieves promising performance on various LLIE benchmark datasets while reducing energy and memory consumption compared to prior approaches. \n\n**[Weaknesses]**\n- In the abstract, the authors claim that Spike-RetinexFormer brings drastic reductions in energy and memory consumption. While it is impressive that the proposed method outperforms RetinexFormer, the reported 6.9% reduction in parameters and a 3.8% increase in FLOPs appear to reflect only incremental improvements. To more convincingly support the claim of energy and memory efficiency, it may be beneficial to highlight the ΔEnergy results presented in Table 4.\n- Although Table 1 demonstrates promising performance, additional comparisons with recent LLIE methods such as GLARE [1*], MambaLLIE [2*], and LLFlow [3*] would strengthen the evaluation.\n- Furthermore, contrary to Lines 260–261, I was unable to find any evaluation on MIT-Adobe FiveK [49] and the five no-reference datasets (LIME [50], MEF [51], DICM [52], VV [53]). Note that LIME [50] is listed twice, and probably citation [4*] appears to be missing.\n- In Lines 295–297, the authors state “the energy consumption can be greatly reduced, since operations are only triggered by spikes rather than every pixel activation”. An ablation study would be helpful to validate the effectiveness of spiking computation in this context. \n- For improved clarity, it would be helpful to provide further explanation of the term “window” mentioned in Line 231. Additional details would assist readers in better understanding its role in the proposed architecture.\n\n\n[1*] Zhou, Han and Dong, Wei and Liu, Xiaohong and Liu, Shuaicheng and Min, Xiongkuo and Zhai, Guangtao and Chen, Jun. Glare: Low light image enhancement via generative latent feature based codebook retrieval. ECCV, 2024.\n\n[2*] Jiangwei Weng and Zhiqiang Yan and Ying Tai and Jianjun Qian and Jian Yang and Jun Li. MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space. NeurIPS, 2024.\n\n[3*] Wang, Yufei and Wan, Renjie and Yang, Wenhan and Li, Haoliang and Chau, Lap-Pui and Kot, Alex C. Low-Light Image Enhancement with Normalizing Flow. AAAI 2022.\n\n[4*]  Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 2013", "was_revised": true, "final_justification": "The authors have kindly addressed the concerns I previously raised. This paper effectively demonstrates the strength of the spike-based architecture in both performance and efficiency.\nAccordingly, I would rate this work as a \"borderline accept\".", "rebuttal": "### Q1: Energy efficiency\n\nOur abstract claims “drastically” reduced energy/memory, yet the actual parameter count reduction is only 6.9% and FLOPs increased by 3.8% compared to RetinexFormer. This is a fair observation by conventional metrics, our model size (1.50M vs 1.61M) is slightly smaller and FLOPs (16.2G vs 15.6G) slightly higher than the RetinexFormer baseline. We will clarify that the **main efficiency gains are in energy** (operations gating) rather than a large static FLOP reduction, and we will highlight the measured ~45% reduction in energy consumption (on GPU simulation, Table 4) when using SNN dynamics. Moreover, our model has a substantially lower memory footprint than heavy CNNs/Transformers (we’re at 1.5M params vs tens of millions for others). \n\nIn the revision, we will emphasize the ΔEnergy results (as suggested) to support the energy efficiency claim. In our ablation Table 4, we report a relative energy metric: for instance, at the chosen operating point T=8, we consider that 100% energy, and found that using fewer time steps can drop energy to 55% or less. Even at **T=8**, our spike-based computation is more energy-efficient in practice than an ANN with the similar FLOPs, because many operations are skipped due to sparsity (neurons only update on spikes). Effectively, even though FLOPs are similar, only a fraction of neurons fire at a given time, leading to lower dynamic power usage. \n\n### Q2: Additional Comparisons with recent LLIE methods \n\nWe thank the reviewer for highlighting this omission. We will incorporate full experimental results for GLARE, MambaLLIE, and LLFlow in the revised manuscript to ensure a comprehensive and fair comparison. Preliminary runs indicate that GLARE, which leverages an external codebook of well‑lit exemplars, achieves very strong PSNR/SSIM on several benchmarks; MambaLLIE, with its Retinex‑inspired global‑then‑local state‑space formulation, also reports gains over recent CNN‑ and Transformer‑based models; and LLFlow, a normalizing‑flow framework, performs competitively in severe low‑light regimes. These methods obtain marginal advantages in conventional image‑quality metrics. Nevertheless, it is important to emphasize that our Spike‑RetinexFormer achieves comparable visual fidelity while dramatically reducing computational and energy costs—only 1.5 M parameters and 16.2 G FLOPs, several‑fold smaller than most Transformer counterparts. Consequently, even against MambaLLIE(2.28 M parameters and 20.85 G FLOPS ), our model maintains a distinct advantage in model compactness and power efficiency. The camera‑ready version will present detailed quantitative comparisons with these methods and analyze the accuracy–efficiency trade‑off, thereby underscoring the practical contributions of our work.\n\n### Q3: Experimental Setup\n\nWe appreciate the reviewer for identifying this discrepancy and sincerely apologize for the oversight. Our original plan was to include a reference‑based evaluation on MIT‑Adobe FiveK and qualitative comparisons on the aforementioned five no‑reference benchmarks in appendix section. Owing to time and page constraints, these experiments were not completed in time for the submission, yet the corresponding sentence remained, resulting in a mismatch between narrative and tables. We acknowledge the confusion this may have caused. In the revised paper, we will add the quantitative FiveK results and qualitative illustrations for no-reference datasets to ensure internal consistency and prevent any potential misunderstanding. We thank the reviewer again for helping us improve the rigor and clarity of the paper.\n\n### Q4: Ablation study\n\nWe agree this is a valuable experiment. While we have indirectly shown that our method vs. RetinexFormer (ANN) indicates comparable quality with fewer operations, we did not include a dedicated ablation isolating “spiking vs. non-spiking”. We will constructed a variant of our network where we replace the LIF neurons with standard ReLU activations and remove the time dimension (essentially an ANN version of our architecture). Preliminary results show that this ANN variant achieves similar PSNR on LOL, but its theoretical operation count (FLOPs) and energy usage are significantly higher (since it has to compute every layer densely for a single time-step equivalent). Due to time constraints and fairness (the ANN version might need re-tuning), we did not include this in the rebuttal now. For the final paper, if allowed, we will at least add a discussion: we’ll explain that spiking vs. non-spiking can be viewed through the lens of activity sparsity. We will also add a small table comparing our model’s ops if it were run as an ANN.\n\nIn summary, while a full new ablation experiment may not fit in the rebuttal, we will strengthen the text to validate the effectiveness of spiking computation, using the evidence we do have (like ΔEnergy and spike rates) and acknowledging this as an important advantage of our approach.\n\n### Q5: Clarification of \"window\"\n\nIn Line 231, “window” refers to the temporal window of length T over which spikes are integrated. In other words, our network processes the input across T discrete time steps (a short temporal window), accumulating the output over time. We will ensure that this use of \"window\" is distinguished from the term “window” in other contexts (window-based attention in Transformers). Given that we also mention \"window MSA\" (windowed multi-head self-attention) elsewhere, the dual use of the word window might be confusing. Thus, we’ll explicitly state “temporal window” whenever referring to the time steps, and use “local window” when referring to the attention mechanism."}]

Author rebuttal:

Meta-review: The authors present a low light image enhancement algorithm, which integrated spiking neural networks in a Retinex-based transformer framework. The authors argue that this combination with spiking networks achieves comparable performance to existing methods.

Strengths identified by the reviewers include:

1. The authors demonstrate improvements in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM) metrics on most of the benchmark datasets.

2. The model is small, which can have implications for energy efficiency or edge computations

3. Some reviewers indicate that the writing is clear and the paper is well-organized.

4. Comparisons across a wide range of datasets are performed.

Weaknesses identified include:

1. Issues related to the organization of Section 3, which intermixes preliminaries on Retinex-based LLIE framework and SNN techniques with the authors’ proposed contributions.

2. Qualitative evaluations on the SID, SMID, and SDSD low-light datasets are absent

3. Uncertainty as to the extent to which the quality gains are attributable to the RetinexFormer backbone vs SNN integration.

4. Supporting better the energy-efficiency claims, especially with respect to actual throughput on hardware

The authors indicate that they will address these remaining points in the final version of their manuscript

The authors tried to address most of the reviewer concerns in their rebuttal. However not all concerns seem to have been addressed. In particular one reviewer still thinks there is insufficient evaluation and there is not a clear analysis of energy advantage. However the other reviewers' concerns with respect to inference latency and theoretical efficiency were mostly addressed which led these reviewers to raise their scores.

Overall the paper received 1 Borderline reject, 1 Borderline accept, 1 Accept and 1 Strong accept. Given that the remaining weaknesses will be addressed in the final manuscript, I am leaning towards recommending acceptance of the paper.
Decision: Accept (poster)