WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement

TL;DR

This paper introduces WavEnhancer, a novel image enhancement model that combines wavelet transforms and transformers to optimize different frequency components, leading to superior aesthetic results.

Contribution

The paper proposes a transformer-based model operating in the wavelet domain to enhance local details and high-level features simultaneously, unifying frequency domain processing with transformer architecture.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively refines different frequency bands of images

Improves both local details and global features

Abstract

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.