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MSPNet: Multi-stage progressive network for image denoising.
- Source :
-
Neurocomputing . Jan2023, Vol. 517, p71-80. 10p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COMPUTER vision
*FEATURE extraction
*IMAGE denoising
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 517
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 160291946
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.09.098