Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution


Minghong Duan (Fudan University), Linhao Qu (Fudan University), Shaolei Liu (Shanghai Institute of Microsystem and Information Technology), Manning Wang (Fudan University)
The 35th British Machine Vision Conference
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Abstract

Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby features, overlooking the importance of incorporating high-frequency prior information of images, which results in limited performance in reconstructing high-frequency texture details in images. To address this issue, we propose the Local Implicit Wavelet Transformer (LIWT) to enhance the restoration of high-frequency texture details. Specifically, we decompose the features extracted by an encoder into four sub-bands containing different frequency information using Discrete Wavelet Transform (DWT). We then introduce the Wavelet Enhanced Residual Module (WERM) to transform these four sub-bands into high-frequency priors, followed by utilizing Wavelet Mutual Projected Fusion (WMPF) and Wavelet-aware Implicit Attention (WIA) to fully exploit the high-frequency prior information for recovering high-frequency details in images. We conducted extensive experiments on benchmark datasets to validate the effectiveness of LIWT. Both qualitative and quantitative results demonstrate that LIWT achieves promising performance in arbitrary-scale SR tasks, outperforming other state-of-the-art methods.

Citation

@inproceedings{Duan_2024_BMVC,
author    = {Minghong Duan and Linhao Qu and Shaolei Liu and Manning Wang},
title     = {Local Implicit Wavelet Transformer for Arbitrary-Scale 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/0041.pdf}
}


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