Low-light image enhancement is challenging in that it needs to consider not only brightness
recovery but also complex issues like color distortion and noise, which usually hide in the …

Deep learning-based methods for low-light image enhancement have achieved remarkable
success. However, the requirement of enormous paired real data limits the generality of …

Absence of nearby light sources while capturing an image will degrade the visibility and
quality of the captured image, making computer vision tasks difficult. In this paper, a color …

The captured images under low-light conditions often suffer insufficient brightness and
notorious noise. Hence, low-light image enhancement is a key challenging task in computer …

In this paper, we rethink the low-light image enhancement task and propose a physically
explainable and generative diffusion model for low-light image enhancement, termed as Diff …

Low-light image enhancement focus on refining the illumination and keep naturalness to
obtain the normal-light image. Current low-light image enhancement methods can well …

Images captured in low-light environments often suffer from complex degradation. Simply
adjusting light would inevitably result in burst of hidden noise and color distortion. To seek …

This paper presents a novel network structure with illumination-aware gamma correction
and complete image modelling to solve the low-light image enhancement problem. Low …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …

Recent approaches for low-light image enhancement achieve excellent performance
through supervised learning based on convolutional neural networks. However, it is still …