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.