2025· Neural Information Processing Systems· 0 citations· 58 references
Computer Science
TL;DR
This 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.
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.
This work introduces a Temporal Feedback Coding (TFC) scheme that leverages feedback at the encoding stage to diversify spike patterns and designs a Global-Local Dynamic LIF (GLD-LIF) neuron that enhances cross-step dependency modeling by integrating local aggregation and global initialization.
Experiments on HDRTV1K show that Bio-SFT achieves competitive perceptual quality and consistently improves HDR-VDP-3 and $\Delta E_{ITP}$ while reducing artifact propagation in symmetric guidance pipelines.
Tingyu Cheng, Ting Zhang, Chongyi Li et al.· 0 citations
A boosted spiking neural network with retina mechanism, termed ReBo-SNN, for the reconstruction of hazy images that draws inspiration from the antagonistic mechanisms of retinal neurons and innovatively devises a retinal module to circumvent the issue of excessive smoothing.
Ya Zhang, Zhitong Zhao, Qian Sun et al.· Neural Networks· 0 citations
CIS-Fuse is proposed, a spiking network that performs cross-modal fusion directly at the membrane-potential level while inheriting the energy efficiency of spike-based computation, with roughly an order of magnitude lower inference energy than the similarly-sized ANN-based DCEvo.
Rui Zhao, Zhuoyuan Li, Wenrui Li et al.· 0 citations
A Spiking Neural Network (SNN) is a kind of brain-inspired and event-driven network, which is becoming a promising energy-efficient alternative to Artificial Neural Networks (ANNs). In recent years, SNN methods have been successfully applied in the fields of electromagnetic signal processing and image signal processing...
Chongxiao Qu, Qian Zhang, Chenxiao Dou et al.· Italian National Conference...· 0 citations
An Adaptive Retinex Model (ARM) is proposed that decomposes an input image into structural (reflectance) and illumination components without relying on handcrafted priors or regularization terms and consistently outperforms state-of-the-art LLIE methods in terms of quantitative metrics and visual quality.
