MSPNet: Multi-stage progressive network for image denoising

Image denoising which aims to restore a high-quality image from the noisy version is one of the most challenging tasks in the low-level computer vision tasks. In this paper, we propose a multi-stage progressive denoising network (MSPNet) and decompose the denoising task into some sub-tasks to progressively remove noise. Specifically, MSPNet is composed of three denoising stages. Each stage combines a feature extraction module (FEM) and a mutual-learning fusion module (MFM). In the feature extraction module, an encoder-decoder architecture is employed to learn non-local contextualized features, and the channel attention blocks (CAB) are utilized to retain the local information of the image. In the mutual-learning fusion module, the criss-cross attention is introduced to balance the image spatial details and the contextualized information. Compared with the state-of-the-art works, experimental results show that MSPNet achieves notable improvements on both objective and subjective evaluations.