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 progre...
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Published in | Neurocomputing (Amsterdam) Vol. 517; pp. 71 - 80 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
14.01.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0925-2312 1872-8286 |
DOI: | 10.1016/j.neucom.2022.09.098 |