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Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features
March 14, 2024, 4:47 a.m. | Yuming Huang, Yingpin Chen, Changhui Wu, Hanrong Xie, Binhui Song, Hui Wang
cs.CV updates on arXiv.org arxiv.org
Abstract: The Swin Transformer image super-resolution reconstruction network only relies on the long-range relationship of window attention and shifted window attention to explore features. This mechanism has two limitations. On the one hand, it only focuses on global features while ignoring local features. On the other hand, it is only concerned with spatial feature interactions while ignoring channel features and channel interactions, thus limiting its non-linear mapping ability. To address the above limitations, this paper proposes …
abstract aggregation arxiv attention cs.cv explore features global image limitations network relationship swin swin transformer transformer type via
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