Title: Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spiking Neural Networks

Low-light Image Enhancement
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

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-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.

1 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 on heuristic image processing (gamma correction, histogram equalization) and the Retinex theory of human vision [1], which decomposes an image into reflectance and illumination. Traditional Retinex-based algorithms [2, 3] 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 to 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 to decompose an image into reflectance and illumination and enhance them with

HxWxC

HxWxC

HxWx

x

x2C

HxWx

44

x4C

Estimator

lumination

Spiking Conv

Ilu

Spiking IG-Attention

Spiking IG-Attention

SpikingIG-Attention

Block (SIGAB)

Block (SIGAB)

Block (SIGAB)

Light Up

×2

×2

3x3Conv

×2

4×4Conv

x2

4x4Conv

Hadamard Addition

Hadamard Product

Concat-Conv

Concat-Conv

Concatenation

HxWxC

HxWxC

Spiking Conv

Spiking Conv

Spiking Conv

len

Spiking IG-Attention

Spiking IG-Attention

Block (SIGAB)

Block (SIGAB)

x2

×2

Concat-Conv

Conv1x1

HxWxC

22

Target Image

Enhanced Image

3x3Conv

2×2Deconv

2x2Deconv

Corruption Restorer

deep networks. Many subsequent methods built on this idea, proposing improved Retinex-based networks with attention or refinement modules [7, 8, 9]. Unsupervised and zero-reference techniques have also emerged: Zero-DCE [10, 11] optimizes a deep curve mapping without ground truth, while EnlightenGAN [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-based architectures [15] to capture global illumination context. Despite improved enhancement quality, most deep LLIE methods are computationally heavy and energy-demanding[16, 17], which limits deployment on resource-constrained devices.

Meanwhile, SNNs have gained interest as the third generation of neural networks, offering eventdriven computation inspired by biological neurons [18]. 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 a continuous-tone regression problem (enhancing pixel intensities), whereas most prior SNN works focused on classification or recognition with discrete labels. Directly applying SNNs to image enhancement must handle fine-grained color and illumination adjustments, requiring high precision despite the coarse spike-based computations. [22] encoded pixel intensities into spike latencies and used a recurrent SNN (with ConvLSTM) for unsupervised LLIE. This demonstrates that SNNs can gradually capture image structure via spike timing.

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 LLIE, 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 [27, 28, 29, 30, 31], mainly for classification or sequential data processing. These spiking Transformers achieve comparable accuracy to standard Transformers while greatly reducing Multiply-Accumulate operations via spike-based attention. This progress motivates our approach to design a spiking Transformer for LLIE.

In this paper, we propose Spike-RetinexFormer, a SNN architecture for LLIE. Spike-RetinexFormer is a spike-driven variant of RetinexFormer that re-instantiates the one-stage Retinex parameterization with temporal spike coding, Leaky Integrate-and-Fire (LIF [32]) neurons, surrogate-gradient training,

and an event-driven attention mechanism. To our knowledge, this is among the first feedforward spiking Transformer-style 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 a Retinex restoration module employs spiking illumination-guided 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-RetinexFormer, which instantiates a one-stage Retinex enhancement pipeline entirely using spiking primitives and is trained with surrogate gradients, demonstrating that continuous image restoration can be effectively addressed in the spike domain.

We develop a spiking illumination-guided attention mechanism-implemented via spikecoincidence affinities, illumination-gated 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, Spike-RetinexFormer 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 [1], 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 to 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 LLNet [4] and LightenNet [5], utilized large-scale datasets (LOL [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 [10] and its subsequent refinement [11] utilized non-reference loss functions, while EnlightenGAN [12] 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 [13] and diffusion probabilistic models [14] have demonstrated capabilities for high-fidelity image enhancement, albeit often at a 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 a related vein, Tang et al. [36] introduced a method focusing on disentangling various image components to achieve more flexible and controllable enhancement.

2.2 Spiking Neural Networks

SNNs are a class of biologically-inspired computational models that process information through discrete temporal events, termed spikes, contrasting with the continuous activation values characteristic of conventional Artificial Neural Networks (ANNs)[37]. Within SNNs, individual neurons integrate input currents over time, generating an output spike when their internal membrane potential surpasses

a predefined threshold. This event-driven operational paradigm offers the potential for substantial energy efficiency, as computationally expensive multiply-accumulate operations prevalent in ANNs are often replaced by simpler arithmetic operations (additions), and power consumption is predominantly associated with active spiking events. Maass [18] conceptualized SNNs as the third generation of neural network models, and subsequent research has established their viability as an alternative to ANNs in 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-differentiable nature of the spike generation mechanism. However, methodologies such as ANN-to-SNN conversion and surrogate gradient learning have facilitated the effective training of deep SNN architectures [20].Consequently, SNNs have demonstrated competitive performance on established image classification benchmarks [38, 20, 39, 40, 41, 42] and have been successfully applied to more complex vision tasks, including object detection (Spiking-YOLO [21]). Notwithstanding these advancements, the application of SNNs to low-level vision tasks requiring continuous-valued outputs, such as image enhancement, remains relatively underexplored. A principal challenge in this context is the generation 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 a 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.

3 Method

3.1 Spiking Retinex-Based Enhancement Framework

Following Retinex theory, a low-light RGB image I ∈ [0 , 1] H × W × 3 is modeled as the Hadamard product of a reflectance image R ∈ [0 , 1] H × W × 3 and an illumination map L ∈ [0 , 1] H × W :

Directly obtaining a lit image by element-wise division is numerically fragile when L is small. Instead, we introduce a light-up map ¯ L that approximates L -1 and enforces the constraint ¯ L ⊙ L ≃ 1 . Multiplying by ¯ L yields a lit-up image

where C ∈ R H × W × 3 collects the overall corruption introduced by sensor noise, quantization, color shifts, and the light-up process itself. We realize (2) in one stage with a spiking illumination estimator and a spiking corruption restorer. We adopt a one-stage Retinex-based framework with two spike-driven modules:

where E denotes the spiking illumination estimator and G the spiking corruption restorer . The illumination prior L p ∈ R H × W × 1 is the channel-average mean of the input image:

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, E is a shallow spike-driven CNN that maps [ I ∥ L p ] ∈ R H × W × 4 to a light-up image and illumination features:

C1: 1 × 1 fusion → C2: depth-wise 5 × 5 (light-up features) → C3: 1 × 1 projection .

G is a 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 F lu (from (3)), injecting long-range, lighting-aware context without dense softmax attention. A lightweight spike-driven head predicts a residual ∆ I so that

The whole pipeline (3) is 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 of T steps for both training and inference. For each layer ℓ and time step t ∈{ 1 , . . . , T } ,

with H ( ℓ ) [0] = 0 , leak λ ( ℓ ) ∈ (0 , 1) , and a fixed threshold V ( ℓ ) th . 2

Input encoding and T -normalization.

The illumination estimator E (see (3)) is executed first and receives a time-shared, T -normalized current

where ∥ denotes channel concatenation. After E predicts the light-up map ¯ L and features F lu (cf. (3)), we form I lu = I ⊙ ¯ L (cf. (2)) and its prior L lu p = mean( I lu ) . The decoder G then uses

which keeps the total injected charge approximately invariant to T . We use ζ =6 and ( κ, κ p )=(1 . 0 , 0 . 5) in all experiments.

Illumination-guided FiLM. For ℓ ≥ 1 in the decoder G , the synaptic current is a convolution on spikes modulated by per-channel FiLM parameters:

where ∗ is a standard convolution and α ( ℓ ) , β ( ℓ ) ∈ R C ℓ are time-shared (constant over t ) and broadcast spatially. They are predicted once from temporally aggregated illumination features (from (3)):

with ( α min , α max ) = (0 . 5 , 2 . 0) , s = 0 . 05 , and σ ( · ) the logistic sigmoid. When F lu is lowerresolution, we bilinearly upsample inside the FiLM head; BatchNorm (if used) runs in eval mode with EMA statistics to remain stable under sparse spikes.

Surrogate gradients and readout. We train end-to-end with a simple piecewise-linear surrogate derivative around the firing threshold. The network uses a single-step readout identical in training and inference:

where Head is a 1 × 1 convolution to 3 channels; gradients through clip use a straight-through estimator on [0 , 1] .

Objective. The loss combines an L 1 image term, a firing-rate regularizer, and a temporal TV on the pre-readout state to suppress flicker without over-smoothing spatial details:

3.3 Illumination-Guided Spiking Transformer

We build on the U -Net -style backbone summarized in Sec. 3.1 and Fig. 1. In this subsection we focus on how illumination guidance is injected at the decoder scales: the scale-aligned illumination features F lu 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 features-modulate 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 in 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-aware context aggregator.

SIGA mechanism: Each SIGA takes spike-encoded query, key, and value streams ( Q [ t ] , K [ t ] , V [ t ] ) and a scale-aligned illumination feature F lu . Multi-head processing is used: channels are split into H heads, computed in parallel, and concatenated. For each head, F lu drives FiLM-style parameters that modulate the Q/K/V projections and set illumination-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 T steps.

Linear

Qx:[0,1]

NxC

[0,1]

N × 1

Linear

Kx:[0,1]

N

L

NxC

Ilumination

InputX

features

Linear

Vx:[0,1]

NxC

NxC

To compute attention in SIGA, we forego explicit floating-point matrix multiplication and softmax. Instead, spike-driven interactions implement hard gating: when a query neuron at position i fires at time t , synapses to candidate keys are activated; if a key neuron at position j simultaneously fires, the coincidence Q i [ t ]=1 ∧ K j [ t ]=1 opens an instantaneous hard gate A ij [ t ] whose threshold is modulated by illumination features. Routing uses the current A ij [ t ] (preserving causality), while optional co-firing accumulators over t =1 . . .T integrate statistics. Owing to spike sparsity (and optionally a local neighborhood), each query selects a 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 t , value spikes are routed accordingly: a value spike V j [ t ] contributes to query position i iff A ij [ t ] is open. Implementationally, each head employs LIF neurons receiving value

Input

UR_Retinex

EnlightenGAN

Diff-Retinex

RetinexFormer

Ours

Target

-112

inputs masked by A ij [ t ] ; absent a gate, V j [ t ] has no effect on i . The SIGA head output at position i is 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 FiLM-style bias.

After the attention stage, head outputs are concatenated and passed through a spiking feed-forward module (two layers of linear spikes with an intermediate LIF activation), analogous to a Transformer FFN. Residual connections wrap the SIGA+FFN block with proper temporal alignment-residuals are added in the membrane-potential 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-guided spiking attention supplies long-range, lighting-aware context while preserving event-driven efficiency.

4 Experiments

4.1 Experimental Setup

We evaluate Spike-RetinexFormer 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) [47], MIT-Adobe FiveK [48], and LIME[49] which commonly used for qualitative evaluation. For datasets with paired ground truth, we train a 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 in Sec. 3. Unless otherwise stated, we use the AdamW optimizer (base learning rate 2 × 10 -4 ) with cosine annealing (optional warm-up), unrolling the spiking dynamics for T time steps and applying global-norm clipping at 1 . 0 . Each model is trained until the validation performance saturates. We report the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) [50] on sRGB results for quantitative comparison. For model complexity, we provide parameter counts (Millions) and FLOPs at 256 × 256 input resolution.

4.2 Comparison with ANN models

We compare Spike-RetinexFormer with recent ANN methods, including Retinex-based CNNs and Transformer-based models: RetinexNet [6], KinD [51], 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. 1 (LOL) and Tab. 2 (SID, SMID, SDSD). Overall, Spike-RetinexFormer delivers competitive performance across most datasets and often matches strong ANN baselines. Notably, on the widely used LOL-v1 dataset, our method attains

25.5 dB PSNR and 0.842 SSIM, which is on par with RetinexFormer (25.2 dB, 0.845) and clearly ahead of earlier Retinex-based CNNs such as RetinexNet (16.8 dB) and KinD (20.8 dB). On the more challenging LOL-v2 (synthetic), Spike-RetinexFormer reaches 26.5 dB PSNR, representing a 0.8 dB gain over RetinexFormer with comparable SSIM; trends on SID/SMID/SDSD are similar. We attribute this competitiveness to the synergy between Retinex decomposition and spiking neural dynamics: illumination-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; as shown in Fig. 4, our method yields natural, well-exposed images with preserved details, whereas some baselines may over-smooth or leave residual noise.

As seen in Tab. 1 and 2, our spiking approach shows competitive or improved performance across the LOL variants and the extremely dark, noisy datasets (SID, SMID, SDSD). On average, SpikeRetinexFormer improves PSNR by ∼ 0 . 5 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 16.2 G FLOPs-noticeably lower than heavy Transformers such as Restormer (26M, 144G). We 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 to decrease because operations are triggered by spikes rather than dense activations. For example, if a 32-bit MAC costs an order of magnitude more energy than an accumulate (AC) [19], and only ∼ 18% of neurons fire per time step in our network (Tab. 5), a back-of-the-envelope estimate suggests on-the-order-of 5 × -10 × energy reduction versus comparable ANN models under these assumptions. While these savings are theoretical (we have not measured power on a neuromorphic chip), they highlight the potential advantage of event-driven processing for power efficiency.

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-v1 using a single RTX 3090.

Tab. 3 compares five progressively enhanced variants. Baseline is a pure spiking U-Net (no illumination branch, no SIGA). Introducing the illumination estimator and light-up operation (+ ORF) yields a +1.45 dB PSNR gain (23.11 → 24.56), indicating that explicit exposure modeling is critical in the spiking regime. Augmenting the ORF backbone with either a local-window MSA (+ W-MSA) or a 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-aware gating improves detail recovery and noise suppression.

Weablate neuron types on LOL-v1 ( T =8 ) with: (i) ANN RetinexFormer; (ii) RetinexFormer with LIF (ReLU → LIF); (iii) Spike-RetinexFormer (LIF+ORF+SIGA); and (iv) the same spiking backbone with IF/PLIF/CLIF (Tab. 4). Swapping ReLU → LIF in the ANN cuts compute energy to 7 . 5 mJ (10.5% of baseline) but reduces fidelity (PSNR -4 . 85 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 ≈ ANN, and 16 . 7 mJ (down from 71 . 6 mJ , -77% ). Fixing the architecture, neuron choice yields small but consistent gaps: CLIF is best (25.61/0.848) at energy close to LIF; IF/PLIF are slightly lower, 15 . 8 -18 . 9 mJ .

Tab. 5 reports performance and efficiency under varying time-step configurations. Using fewer steps ( T =4 ) provides limited temporal evidence, leading to a modest PSNR drop and a higher average spike firing rate (SFR). Increasing to T =8 achieves a favorable accuracy-latency balance; pushing to T =12 offers only

marginal PSNR gains while increasing wall-time nearly linearly. We therefore adopt T =8 as the default trade-off.

5 Limitations and Future Work

Hardware-validated efficiency and scalability. Although SIGA avoids dense softmax attention, computing spike co-firing statistics can still scale as O ( N 2 ) in the number of spatial tokens N when firing rates increase or sparsity collapses in high-illumination regions. To address this, we will design an event-driven sparse attention kernel 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 topk gating, block/tile-level sparsity, and kernel-level fusion of FiLM-style illumination modulation to reduce compute, memory traffic, and latency, and will release kernels and power traces to facilitate reproducibility.

Robustness and cross-domain generalization. We will pursue RAW-aware training via a differentiable 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-aware exposure control that disentangles aleatoric and epistemic components to mitigate over- and under-enhancement. We also plan to extend the framework to video using temporal spikeconsistency 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.

6 Conclusion

We introduced Spike-RetinexFormer, a low-light image enhancer that unifies spiking neural networks with a Retinex-inspired Transformer architecture. By guiding spiking self-attention with an estimated illumination map, our approach achieves competitive enhancement quality on 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-RetinexFormer offers a practical blueprint for high-performance, energy-frugal vision systems on power-constrained platforms and points toward hardware-validated evaluation and video extensions.

References

Edwin H Land and John J McCann. Lightness and retinex theory. Journal of the Optical society of America , 61(1):1-11, 1971.

Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. Properties and performance of a center/surround retinex. IEEE transactions on image processing , 6(3):451-462, 1997.

Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE Transactions on image processing , 26(2):982-993, 2016.

Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition , 61:650-662, 2017.

Chongyi Li, Jichang Guo, Fatih Porikli, and Yanwei Pang. Lightennet: A convolutional neural network for weakly illuminated image enhancement. Pattern recognition letters , 104:15-22, 2018.

Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 , 2018.

Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, and Haiying Wang. A switched view of retinex: Deep self-regularized low-light image enhancement. Neurocomputing , 454:361-372, 2021.

Hao Tang, Hongyu Zhu, Huanjie Tao, and Chao Xie. An improved algorithm for low-light image enhancement based on retinexnet. Applied Sciences , 12(14):7268, 2022.

Shuai Xu, Jian Zhang, Xin Qin, Yuchen Xiao, Jianjun Qian, Liling Bo, Heng Zhang, Hongran Li, and Zhaoman Zhong. Deep retinex decomposition network for underwater image enhancement. Computers and Electrical Engineering , 100:107822, 2022.

Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1780-1789, 2020.

Chongyi Li, Chunle Guo, and Chen Change Loy. Learning to enhance low-light image via zero-reference deep curve estimation. IEEE transactions on pattern analysis and machine intelligence , 44(8):4225-4238, 2021.

Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. Enlightengan: Deep light enhancement without paired supervision. IEEE transactions on image processing , 30:2340-2349, 2021.

Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot. Low-light image enhancement with normalizing flow. In Proceedings of the AAAI conference on artificial intelligence , volume 36, pages 2604-2612, 2022.

Dewei Zhou, Zongxin Yang, and Yi Yang. Pyramid diffusion models for low-light image enhancement. arXiv preprint arXiv:2305.10028 , 2023.

Nanfeng Jiang, Junhong Lin, Ting Zhang, Haifeng Zheng, and Tiesong Zhao. Low-light image enhancement via stage-transformer-guided network. IEEE Transactions on Circuits and Systems for Video Technology , 33(8):3701-3712, 2023.

Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low-light image and video enhancement using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence , 44(12):9396-9416, 2021.

Jiawei Guo, Jieming Ma, Ángel F García-Fernández, Yungang Zhang, and Haining Liang. A survey on image enhancement for low-light images. Heliyon , 9(4):e14558, 2023.

Wolfgang Maass. Networks of spiking neurons: the third generation of neural network models. Neural networks , 10(9):1659-1671, 1997.

Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. Towards spike-based machine intelligence with neuromorphic computing. Nature , 575(7784):607-617, 2019.

Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi. Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI conference on artificial intelligence , volume 33, pages 1311-1318, 2019.

Seijoon Kim, Seongsik Park, Byunggook Na, and Sungroh Yoon. Spiking-yolo: spiking neural network for energy-efficient object detection. In Proceedings of the AAAI conference on artificial intelligence , volume 34, pages 11270-11277, 2020.

Xinghao Wang, Qiang Wang, Lei Zhang, Yi Qu, Fan Yi, Jiayang Yu, Qiuhan Liu, Ruicong Xia, Ziling Xu, and Sirong Tong. Dcenet-based low-light image enhancement improved by spiking encoding and convlstm. Frontiers in Neuroscience , 18:1297671, 2024.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017.

Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR) , 54(10s):1-41, 2022.

Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 12299-12310, 2021.

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5728-5739, 2022.

Etienne Mueller, Viktor Studenyak, Daniel Auge, and Alois Knoll. Spiking transformer networks: A rate coded approach for processing sequential data. In 2021 7th International Conference on Systems and Informatics (ICSAI) , pages 1-5. IEEE, 2021.

Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, Zhaoxu Yang, and Guoqi Li. Temporal-wise attention spiking neural networks for event streams classification. In Proceedings of the IEEE/CVF international conference on computer vision , pages 10221-10230, 2021.

Ziqing Wang, Yuetong Fang, Jiahang Cao, Qiang Zhang, Zhongrui Wang, and Renjing Xu. Masked spiking transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 1761-1771, 2023.

Zhaokun Zhou, Yijie Lu, Yanhao Jia, Kaiwei Che, Jun Niu, Liwei Huang, Xinyu Shi, Yuesheng Zhu, Guoqi Li, Zhaofei Yu, et al. Spiking transformer with experts mixture. Advances in Neural Information Processing Systems , 37:10036-10059, 2024.

Xiaotian Song, Andy Song, Rong Xiao, and Yanan Sun. One-step spiking transformer with a linear complexity. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence , pages 3142-3150, 2024.

Wulfram Gerstner and Werner M Kistler. Spiking neuron models: Single neurons, populations, plasticity . Cambridge university press, 2002.

Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslrquality photos on mobile devices with deep convolutional networks. In Proceedings of the IEEE international conference on computer vision , pages 3277-3285, 2017.

Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, and Bo Zhang. Deepexposure: Learning to expose photos with asynchronously reinforced adversarial learning. Advances in neural information processing systems , 31, 2018.

Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 3063-3072, 2020.

Linfeng Tang, Jiayi Ma, Hao Zhang, and Xiaojie Guo. Drlie: Flexible low-light image enhancement via disentangled representations. IEEE transactions on neural networks and learning systems , 35(2):2694-2707, 2022.

Hanwei Zhang, Ying Zhu, Dan Wang, Lijun Zhang, Tianxiang Chen, Ziyang Wang, and Zi Ye. A survey on visual mamba. Applied Sciences , 14(13):5683, 2024.

Zhaokun Zhou, Yuesheng Zhu, Chao He, Yaowei Wang, Shuicheng Yan, Yonghong Tian, and Li Yuan. Spikformer: When spiking neural network meets transformer. arXiv preprint arXiv:2209.15425 , 2022.

Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, and Guoqi Li. Spikedriven transformer. Advances in neural information processing systems , 36:64043-64058, 2023.

Man Yao, Jiakui Hu, Tianxiang Hu, Yifan Xu, Zhaokun Zhou, Yonghong Tian, Bo Xu, and Guoqi Li. Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips. arXiv preprint arXiv:2404.03663 , 2024.

Man Yao, Xuerui Qiu, Tianxiang Hu, Jiakui Hu, Yuhong Chou, Keyu Tian, Jianxing Liao, Luziwei Leng, Bo Xu, and Guoqi Li. Scaling spike-driven transformer with efficient spike firing approximation training. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025.

Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Liwei Huang, Xiaopeng Fan, Li Yuan, Zhengyu Ma, Huihui Zhou, and Yonghong Tian. Qkformer: Hierarchical spiking transformer using qk attention. arXiv preprint arXiv:2403.16552 , 2024.

Yuanhao Cai, Hao Bian, Jing Lin, Haoqian Wang, Radu Timofte, and Yulun Zhang. Retinexformer: One-stage retinex-based transformer for low-light image enhancement. In Proceedings of the IEEE/CVF international conference on computer vision , pages 12504-12513, 2023.

Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Transactions on Image Processing , 30:2072-2086, 2021.

Chen Chen, Qifeng Chen, Minh N Do, and Vladlen Koltun. Seeing motion in the dark. In Proceedings of the IEEE/CVF International conference on computer vision , pages 3185-3194, 2019.

Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 32913300, 2018.

Ruixing Wang, Xiaogang Xu, Chi-Wing Fu, Jiangbo Lu, Bei Yu, and Jiaya Jia. Seeing dynamic scene in the dark: A high-quality video dataset with mechatronic alignment. In Proceedings of the IEEE/CVF international conference on computer vision , pages 9700-9709, 2021.

Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tonal adjustment with a database of input/output image pairs. In CVPR 2011 , pages 97-104. IEEE, 2011.

Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE Transactions on image processing , 26(2):982-993, 2016.

Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th international conference on pattern recognition , pages 2366-2369. IEEE, 2010.

Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the 27th ACM international conference on multimedia , pages 1632-1640, 2019.

Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6849-6857, 2019.

Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10561-10570, 2021.

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXV 16 , pages 492-511. Springer, 2020.

Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 17683-17693, 2022.

Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. Snr-aware low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 17714-17724, 2022.

Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian. Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In Proceedings of the IEEE/CVF international conference on computer vision , pages 2661-2671, 2021.

Yulong Huang, Xiaopeng Lin, Hongwei Ren, Haotian Fu, Yue Zhou, Zunchang Liu, Biao Pan, and Bojun Cheng. Clif: Complementary leaky integrate-and-fire neuron for spiking neural networks. arXiv preprint arXiv:2402.04663 , 2024.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: [TODO]

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: [TODO]

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer:[Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: [TODO]

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: No potential positive societal impacts and negative societal impacts.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to

generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification:

[TODO]

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: [TODO]

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**[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\n\nQUESTIONS:\nPlease refer to Weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nSpike-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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-06-30"}