Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

TL;DR

This paper introduces a novel frequency domain-based transformer framework that unifies low-light enhancement, exposure correction, and multi-exposure fusion, achieving state-of-the-art results in image restoration tasks.

Contribution

It proposes a holistic frequency attention mechanism and a dynamic frequency transformer that unify multiple exposure correction tasks in a single model.

Findings

Achieves state-of-the-art performance on mainstream datasets.

Effectively unifies low-light enhancement, exposure correction, and multi-exposure fusion.

Demonstrates improved image restoration quality across various exposure-related tasks.

Abstract

The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction.