关键词: 参考图像, 超分辨率, Transformer, 超大倍率, 注意力

Abstract: Image super-resolution （SR） refers to reconstructing the corresponding high-resolution copy from a low-resolution image. Aiming at solving the problem of inaccurate reconstruction of SR in the cases of super-large magnification （8×, 16×）, a multi-level transformer super-magnification reconstruction network is proposed （MTLF）. MTLF performs multi-level stacking of multiple transformers to process features of different scales, and uses the attention weights, which are obtained by the transformer and then improved by the modified attention module, to synthesize finer textures. In the end, the features of all magnifications fuse into a super-large-scale SR image. Experiments resenlts show that MTLF is superior to the state-of-the-art methods （including single-image super-resolution and Ref-based super-resolution methods） in terms of peak signal-to-noise ratio and visual effects. In particular, MTLF achieves fairly good results in the ultimate magnification （32×） scenario.

Key words: reference-based, super-resolution, transformer, large-factor, attention