用脉冲神经网络结合小波变换，实现高效低功耗图像修复。
Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration
- 设计脉冲双金字塔小波模块，捕捉长距离依赖关系。
- 在多个基准上降低计算成本与能耗，保持图像质量。
- 适合资源受限设备的实时图像修复应用。
脉冲神经网络（SNN）因其高效性与生物启发性，在计算机视觉中备受关注。尽管基于脉冲卷积神经网络的方法在图像修复任务中展现出潜力，但其性能受限于卷积操作固有的感受野局限。本文探索离散小波变换的优势，提出一种基于脉冲金字塔小波的模型（SPWM），以实现高效率、低功耗的目标。具体而言，我们设计了脉冲双金字塔小波（SDPW）模块，用于建模长程依赖，并利用退化过程在小波域中的特性。在多个基准测试上的实验结果表明，SPWM显著降低了计算开销和能量消耗，同时保持了良好的图像质量。该方法展示了SNN在图像修复领域的潜力，为资源受限设备的未来应用提供了新思路。
原文摘要 · Abstract (English)
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.
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