Effcient Shift Network in Denoising-Friendly Space for Real Noise Removal

Lipeng Zhang, Zheng Zhao, Yuan Zhang, Shuaitao Zhang, Di Xie, Shiliang Pu, Hui Mao

Abstract: Recently, following the success of neural networks, image denoising has achieved great improvements. However, it is challenging to construct an efficient denoising model with excellent performance and less computation. In this paper, we propose an extremely lightweight framework to remove real image noise in denoising-friendly space. Specif-ically, we apply the wavelet transform to project the noisy image and feature maps into low and high frequency domain, which decouples the noise information from the clean ones to a certain extent, thereby reducing the difficulty of denoising task. In addition, we further introduce a lightweight op-erator called Grouped Shift Module (GSM) into our denoising network, hence much heavy computation can be saved. Experimental results on the current benchmark demonstrate that our Wavelet Shift Denoising Network (WSNet) even achieves PSNR 39.28 dB with only 3G FLOPs on the SIDD benchmark. Our source code and models are available at https://github.com/HIK-DLSlimIWSNet.