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|a 10.1109/TIP.2024.3451939
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|a eng
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| 100 |
1 |
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|a Zhang, Yi
|e verfasserin
|4 aut
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| 245 |
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|a Reference-Based Multi-Stage Progressive Restoration for Multi-Degraded Images
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 12.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Image restoration (IR) via deep learning has been vigorously studied in recent years. However, due to the ill-posed nature of the problem, it is challenging to recover the high-quality image details from a single distorted input especially when images are corrupted by multiple distortions. In this paper, we propose a multi-stage IR approach for progressive restoration of multi-degraded images via transferring similar edges/textures from the reference image. Our method, called a Reference-based Image Restoration Transformer (Ref-IRT), operates via three main stages. In the first stage, a cascaded U-Transformer network is employed to perform the preliminary recovery of the image. The proposed network consists of two U-Transformer architectures connected by feature fusion of the encoders and decoders, and the residual image is estimated by each U-Transformer in an easy-to-hard and coarse-to-fine fashion to gradually recover the high-quality image. The second and third stages perform texture transfer from a reference image to the preliminarily-recovered target image to further enhance the restoration performance. To this end, a quality-degradation-restoration method is proposed for more accurate content/texture matching between the reference and target images, and a texture transfer/reconstruction network is employed to map the transferred features to the high-quality image. Experimental results tested on three benchmark datasets demonstrate the effectiveness of our model as compared with other state-of-the-art multi-degraded IR methods. Our code and dataset are available at https://vinelab.jp/refmdir/
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|a Journal Article
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| 700 |
1 |
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|a Yang, Qixue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chandler, Damon M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mou, Xuanqin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 01., Seite 4982-4997
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
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|g volume:33
|g year:2024
|g day:01
|g pages:4982-4997
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|u http://dx.doi.org/10.1109/TIP.2024.3451939
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