Image enhancement is a technique that frequently utilized in digital image processing. In recent years, the popularity of learning-based techniques for enhancing the aesthetic performance of photographs has increased. However, the majority of current works do not optimize an image from different frequency domains and typically focus on either pixel-level or global-level enhancements. In this paper, we propose a transformer-based model in the wavelet domain to refine different frequency bands of an image. Our method focuses both on local details and high-level features for enhancement, which can generate superior results. On the basis of comprehensive benchmark evaluations, our method outperforms the state-of-the-art methods.
@article{arxiv.2212.08327,
title = {WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement},
author = {Zinuo Li and Xuhang Chen and Chi-Man Pun and Shuqiang Wang},
journal= {arXiv preprint arXiv:2212.08327},
year = {2022}
}