Summary of Local Implicit Wavelet Transformer For Arbitrary-scale Super-resolution, by Minghong Duan et al.
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution
by Minghong Duan, Linhao Qu, Shaolei Liu, Manning Wang
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach, Local Implicit Wavelet Transformer (LIWT), to enhance the restoration of high-frequency texture details in images. Building on implicit neural representations for arbitrary-scale Super-Resolution (SR), LIWT incorporates high-frequency prior information using Discrete Wavelet Transform (DWT) and Wavelet Enhanced Residual Module (WERM). The model then utilizes Wavelet Mutual Projected Fusion (WMPF) and Wavelet-aware Implicit Attention (WIA) to fully exploit this prior information. Experimental results on benchmark datasets demonstrate the effectiveness of LIWT, outperforming state-of-the-art methods in arbitrary-scale SR tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve image resolution by using a new way to learn from images. It creates a special model that can restore high-frequency details like texture and patterns in an image. This is important because many current models don’t do this well, especially when the image is very large or small. The model uses something called wavelets to understand these high-frequency details better. By using this new approach, the paper shows that its model can produce much better results than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Attention » Super resolution » Transformer
