Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration
It addresses the need for efficient image restoration on resource-limited devices by leveraging spiking neural networks and wavelet transforms.
The paper proposes a spiking pyramid wavelet model (SPWM) for image restoration that reduces computational costs and energy consumption while maintaining image quality, demonstrating the potential of SNNs for resource-limited devices.
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.