Image super-resolution by fusing strengths of CNN and transformer with CRAFT
Image super-resolution by fusing strengths of CNN and transformer with CRAFT
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
arXiv paper abstract https://arxiv.org/abs/2308.05022
arXiv PDF paper https://arxiv.org/pdf/2308.05022.pdf
Transformer-based methods have ... remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies.
... most ... research ... has prioritized the design of transformer ... to capture global information, while overlooking the importance of incorporating high-frequency priors
... found that transformer structures are more adept at capturing low-frequency information, but have limited capacity in constructing high-frequency representations ... compared to their convolutional counterparts.
... proposed solution, the cross-refinement adaptive feature modulation transformer (CRAFT), integrates the strengths of both convolutional and transformer structures.
It comprises three key components: the high-frequency enhancement residual block (HFERB) for extracting high-frequency information, the shift rectangle window attention block (SRWAB) for capturing global information, and the hybrid fusion block (HFB) for refining the global representation.
... experiments on multiple datasets demonstrate that CRAFT outperforms state-of-the-art methods ... while using fewer parameters ...
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