Amplitude-Guidance Low-Light Image Enhancement with Frequency-based Channel Attention
Jinlong Wang, Xiongxin Tang, Fanjiang Xu, Hanxiang Yang
Abstract
Low-light image enhancement aims to improve lightness and eliminate degradation caused by low light. However, most current methods struggle to effectively handle the mixed degradations of both brightness and structure, leading to structural distortions and insufficient brightness enhancement. Additionally, existing Fourier-based methods learn amplitude and phase independently, yet overlook the intrinsic connection between brightness and structure. In this paper, we propose an amplitude-guidance low-light image enhancement network, which utilizes the Fourier transform to extract the amplitude and phase of images and reconstruct them using the network. Considering that uneven brightness distribution in images can lead to varying levels of structural degradation, we design an Amplitude-Guidance Self-Attention (AGSA) that uses amplitude to guide phase recovery, enabling it to handle different levels of structure degradation. Additionally, to further improve the enhancement capability of our network, we design a Frequency-based Channel Attention (FCA) that preserves more frequency information when compressing channels. Extensive experiments demonstrate the superiority of our proposed network over existing SOTA methods.
BibTeX
@inproceedings{icassp2025_amplitudeguidanc,
title = {Amplitude-Guidance Low-Light Image Enhancement with Frequency-based Channel Attention},
author = {Jinlong Wang and Xiongxin Tang and Fanjiang Xu and Hanxiang Yang},
booktitle = {ICASSP 2025},
year = {2025}
}