Multiscale residual fusion network for image denoising

Abstract

Deep‐learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. The existing deep‐learning methods can be conducted using two major models: Encoder–decoder and high‐resolution, where the high‐resolution model has superior resolution ability for detail description and restoration. In this study, a high‐resolution‐based network called multiscale residual fusion network (MRF‐Net) is proposed, which employed the spatial and contextual information of images. In detail, dilated convolution layers are used to enlarge the network's receptive field and learned sufficient features in a multiscale feature extracting module. The function of dilated convolution is reinterpreted here and it is viewed as a complex downsampling operation. Therefore, multiscale feature analysis could be performed in the proposed network by dilated convolution. Multilevel feature maps are sequentially obtained through a residual projection module, where considerable contextual and spatial information was collected from the multiscale features. In a residual fusion module, all maps were aggregated to generate a residual image effectively for noise removal. Experiments demonstrated that the MRF‐Net outperformed several state‐of‐the‐art model‐based and deep‐learning methods in both blind and non‐blind image denoising tests. Meanwhile, ablation studies were executed to verify the denoising performance of each module. Moreover, this method exhibited high computational efficiency, thus demonstrating its practicability.