A Single Image Deraining Algorithm Based on Swin Transformer
Single image deraining aims to recover the rain-free image from rainy image. Most existing deraining methods based on deep learning do not utilize the global information of rainy image effectively, which makes them lose much detailed and structural information after processing. Focusing on this issu...
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Veröffentlicht in: | Shànghăi jiāotōng dàxué xuébào 2023-05, Vol.57 (5), p.613-623 |
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Format: | Artikel |
Sprache: | chi |
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Zusammenfassung: | Single image deraining aims to recover the rain-free image from rainy image. Most existing deraining methods based on deep learning do not utilize the global information of rainy image effectively, which makes them lose much detailed and structural information after processing. Focusing on this issue, this paper proposes a single image deraining algorithm based on Swin Transformer. The network mainly includes a shallow features extraction module and a deep features extraction network. The former exploits the context information aggregation module to adapt to the distribution diversity of rain streaks and extracts the shallow features of rainy image. The latter uses Swin Transformer to capture the global information and long-distance dependencies between different pixels, in combination with residual convolution and dense connection to strengthen features learning. Finally, the derained image is obtained through a global residual convolution. In addition, this paper proposes a novel comprehensive loss function |
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ISSN: | 1006-2467 |
DOI: | 10.16183/j.cnki.jsjtu.2022.032 |