SFEMGN: Image Denoising with Shallow Feature Enhancement Network and Multi-Scale ConvGRU

Abstract

Image denoising methods based on convolutional neural networks have been popular and achieved relatively excellent performance. However, most of the existing methods cannot fully obtain and use the shallow feature information when removing noise, and cannot better combine information between various network layers. In this paper, we propose an image denoising algorithm based on a feature enhancement network and multi-scale convGRU, named a shallow feature enhancement and multi-scale convGRU denoising network (SFEMGN), through an in-depth study of convolutional networks and GRU networks. We first propose a feature enhancement block to extract richer shallow features and enhance the protection of image details. Furthermore, the proposed SFEMGN integrates a multi-scale convolution GRU module, which can combine spatial features and temporal features at the same time. Comparative experiments and ablation studies demonstrate that our proposed model can achieve competitive performance in both gray and color image denoising tasks.