# Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

arXiv: https://arxiv.org/abs/2411.06442 · HTML: https://mlsanity.com/paper/2411.06442/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2103.12716/) (2021)
- [Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding](https://mlsanity.com/paper/2112.05756/) (2021)
- [An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution](https://mlsanity.com/paper/2401.15613/) (2024)

## Built on by (most cited first, 5 of 5)

- [Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling](https://mlsanity.com/paper/2503.06617/) (2025)
- [Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters](https://mlsanity.com/paper/2504.10929/) (2025)
- [Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution](https://mlsanity.com/paper/2511.01175/) (2025)
- [FLAIR: Frequency- and Locality-Aware Implicit Neural Representations](https://mlsanity.com/paper/2508.13544/) (2025)
- [GB-LSR: Local Spectral Decoding with a Learned Global Bandwidth for Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2606.19617/) (2026)

## Abstract (arXiv)

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.