WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement
This work addresses the problem of limited frequency domain optimization in image enhancement for digital photography, though it appears incremental as it builds on existing transformer and wavelet techniques.
The paper tackles image enhancement by optimizing images across different frequency domains using a transformer-based model in the wavelet domain, resulting in superior performance that outperforms state-of-the-art methods on benchmark evaluations.
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.