Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution


Dinh Phu Tran (Korea Advanced Institute of Science & Technology), Dao Duy Hung (Korea Advanced Institute of Science & Technology), Daeyoung Kim (Korea Advanced Institute of Science and Technology)
The 35th British Machine Vision Conference
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Abstract

Recently, window-based attention methods have shown great potential for computer vision tasks, particularly in Single Image Super-Resolution (SISR). However, it may fall short in capturing long-range dependencies and relationships between distant tokens. Additionally, we find that learning on spatial domain does not convey the frequency content of the image, which is a crucial aspect in SISR. To tackle these issues, we propose a new Channel-Partitioned Attention Transformer (CPAT) to better capture long-range dependencies by sequentially expanding windows along the height and width of feature maps. In addition, we propose a novel Spatial-Frequency Interaction Module (SFIM), which incorporates information from spatial and frequency domains to provide a more comprehensive information from feature maps. This includes information about the frequency content and enhances the receptive field across the entire image. Experimental findings demonstrate the effectiveness of our proposed modules and architecture. In particular, CPAT surpasses current state-of-the-art methods HAT by up to 0.31dB at x2 SR on Urban100.

Citation

@inproceedings{Tran_2024_BMVC,
author    = {Dinh Phu Tran and Dao Duy Hung and Daeyoung Kim},
title     = {Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0432.pdf}
}


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