Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration
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Abstract (arXiv)
Spiking neural networks (SNNs) have garnered significant interest in computer vision due to their potential for efficiency and biological inspiration. While spiking CNN-based methods have shown promise for image restoration (IR) tasks, their performance is constrained by the inherent receptive field limitations of CNN operations. In the paper, we explore the benefits of discrete wavelet transformation and propose a spiking pyramid wavelet-based model (SPWM) for high-efficient and low-energy target. Specifically, we develop a spiking dual pyramid wavelet (SDPW) block to model long-range dependency and exploit the properties of the degradation in the wavelet domain. Experimental results on several benchmarks demonstrate that SPWM significantly lowers computational costs and energy consumption while maintaining image quality. Our method showcases the potential of SNNs in the field of IR, offering new insights for future applications of resource-limited devices.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 52 of its 68 references that are in the map. First posted on arXiv 17 Jun 2026.
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References · 52
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,876
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,622
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,354
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,221
- 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 865
- 2021Multi-Stage Progressive Image Restorationcited by 813
- 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 720
- 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 551
- 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
- 2019Kindling the Darkness: A Practical Low-light Image Enhancercited by 355
- 2016Deep Joint Rain Detection and Removal from a Single Imagecited by 351
- 2019FFA-Net: Feature Fusion Attention Network for Single Image Dehazingcited by 307
38 more
- 2019Progressive Image Deraining Networks: A Better and Simpler Baselinecited by 271
- 2022Spikformer: When Spiking Neural Network Meets Transformercited by 266
- 2018Density-aware Single Image De-raining using a Multi-stream Dense Networkcited by 248
- 2020Multi-Scale Boosted Dehazing Network with Dense Feature Fusioncited by 230
- 2019GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazingcited by 227
- 2023Learning A Sparse Transformer Network for Effective Image Derainingcited by 204
- 2022Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Methodcited by 174
- 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 167
- 2023Low-Light Image Enhancement with Wavelet-based Diffusion Modelscited by 127
- 2020A Model-driven Deep Neural Network for Single Image Rain Removalcited by 125
- 2019Dense Haze: A benchmark for image dehazing with dense-haze and haze-free imagescited by 107
- 2022Attention Spiking Neural Networkscited by 105
- 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 99
- 2023Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restorationcited by 86
- 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
- 2020EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Derainingcited by 39
- 2022Masked Spiking Transformercited by 39
- 2023Xformer: Hybrid X-Shaped Transformer for Image Denoisingcited by 37
- 2024GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrievalcited by 33
- 2025Does FLUX Already Know How to Perform Physically Plausible Image Composition?cited by 30
- 2022DRT: A Lightweight Single Image Deraining Recursive Transformercited by 26
- 2024Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancementcited by 24
- 2025Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazingcited by 23
- 2025From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspectivecited by 22
- 2024Low-Light Image Enhancement via Generative Perceptual Priorscited by 21
- 2022Multi-Scale Adaptive Network for Single Image Denoisingcited by 17
- 2022Magic ELF: Image Deraining Meets Association Learning and Transformercited by 17
- 2024Learning A Spiking Neural Network for Efficient Image Derainingcited by 16
- 2025UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Datasetcited by 16
- 2024ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correctioncited by 15
- 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Expertscited by 14
- 2023Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translationcited by 11
- 2025Learning to See in the Extremely Darkcited by 11
- 2024O-Mamba: O-shape State-Space Model for Underwater Image Enhancementcited by 9
- 2023Dual Residual Attention Network for Image Denoisingcited by 6
- 2025Learning Multi-scale Spatial-frequency Features for Image Denoisingcited by 6
- 2026From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmarkcited by 6
- 2023Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translationcited by 5
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