A Dynamic Network with Transformer for Image Denoising

Abstract

Deep convolutional neural networks (CNNs) can achieve good performance in image denoising due to their superiority in the extraction of structural information. However, they may ignore the relationships between pixels to limit effects for image denoising. Transformer, focusing on pixel to pixel relationships can effectively solve this problem. This article aims to make a CNN and Transformer complement each other in image denoising. In this study, we propose a dynamic network with Transformer for image denoising (DTNet), with a residual block (RB), a multi-head self-attention block (MSAB), and a multidimensional dynamic enhancement block (MDEB). Firstly, the RB not only utilizes a CNN but also lays the foundation for the combination with Transformer. Then, the MSAB adds positional encoding and applies multi-head self-attention, which enables the preservation of sequential positional information while employing the Transformer to obtain global information. Finally, the MDEB uses dimension enhancement and dynamic convolution to improve the adaptive ability. The experiments show that our DTNet is superior to some existing methods for image denoising.