Low-Light Image Enhancement With Multi-Scale Attention and Frequency-Domain Optimization

Abstract

Low-light image enhancement aims to improve the perceptual quality of images captured in conditions of insufficient illumination. However, such images are often characterized by low visibility and noise, making the task challenging. Recently, significant progress has been made using deep learning-based approaches. Nonetheless, existing methods encounter difficulties in balancing global and local illumination enhancement and may fail to suppress noise in complex lighting conditions. To address these issues, we first propose a multi-scale illumination adjustment network to balance both global illumination and local contrast. Furthermore, to effectively suppress noise potentially amplified by the illumination adjustment, we introduce a wavelet-based attention network that efficiently perceives and removes noise in the frequency domain. We additionally incorporate a discrete wavelet transform loss to supervise the training process. Particularly, the proposed wavelet-based attention network has been shown to enhance the performance of existing low-light image enhancement methods. This observation indicates that the proposed wavelet-based attention network can be flexibly adapted to current approaches to yield superior enhancement results. Furthermore, extensive experiments conducted on benchmark datasets and downstream object detection task demonstrate that our proposed method achieves state-of-the-art performance and generalization ability.