Learning Frequency-Aware Representation For Low-light Image Enhancement

Jinlong Wang, Xiongxin Tang

Abstract: The low-light image enhancement (LLIE) aims to improve image brightness and alleviate the degradation caused by low-light conditions. Recently, many researchers have explored the frequency information for LLIE. Within the frequency domain, amplitude indicates brightness, while phase represents structural details. Based on this observation, numerous methods have been proposed to learn features in the Fourier space and achieved impressive performance. However, these methods ignore the interactions between different frequencies and suffer from a cumbersome two-stage training process. In this paper, we introduce a simple yet effective one-stage frequency-aware network, FANet, comprising two core modules: frequency self-attention block (FSAB) and frequency filter block (FFB). FSAB utilizes the self-attention mechanism to separately learn the illumination and structure degradations based on the Fourier prior. Motivated by the fact that different frequencies contribute to LLIE to varying extents, we propose to pay more attention to important frequencies. To this end, the frequency filter mechanism is applied to capture global frequency information in FFB, dynamically focusing on the crucial frequencies and further improving both amplitude and phase features for LLIE. We validate our proposed approach on the LOL-v1, LOL-v2-real, and LOL-v2-synthetic datasets using PSNR and SSIM metrics. The quantitative and qualitative experiments demonstrate the superiority and effectiveness of our method against the other state-of-the-art methods.