NHNet: A non‐local hierarchical network for image denoising

Abstract

With the fast development of deep learning models, hierarchical convolutional neural networks have achieved great success in image denoising tasks. To further boost the performance of image denoising, a novel non-local hierarchical network (NHNet) is proposed. Unlike existing U-Net-based hierarchical methods, which mainly focus on downsampling operations, NHNet adopts an initial resolution path and a high resolution path. Specifically, the high-resolution features are obtained through upsampling, where the non-local mechanism is adopted to capture the self-similarity properties, which contribute to a better denoising performance. Cross connections and channel attention layers are added between the two paths to integrate features in different resolutions. Compared with other U-Net-based hierarchical networks, NHNet requires fewer parameters. Experiments show that NHNet achieves state-of-the-art performance in Gaussian denoising tasks and gets competitive results when dealing with real image denoising.