Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

• URL: http://arxiv.org/abs/2411.06442v1
• Date: Sun, 10 Nov 2024 12:21:14 GMT
• Title: Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution
• Authors: Minghong Duan, Linhao Qu, Shaolei Liu, Manning Wang,
• Abstract summary: 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. We propose the Local Implicit Wavelet Transformer (LIWT) to enhance the restoration of high-frequency texture details.
• Score: 15.610136214020947
• License:
• 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 in 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 the Wavelet Mutual Projected Fusion (WMPF) and the 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. The code is available at https://github.com/dmhdmhdmh/LIWT.

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