Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …

In real scenes, due to the problems of low light and unsuitable views, the images often
exhibit a variety of degradations, such as low contrast, color distortion, and noise. These …

When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …

As the quality of optical sensors improves, there is a need for processing large-scale
images. In particular, the ability of devices to capture ultra-high definition (UHD) images and …

Abstract Low-light Image Enhancement (LIE) aims at improving contrast and restoring
details for images captured in low-light conditions. Most of the previous LIE algorithms adjust …

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 …

Diffusion models have achieved promising results in image restoration tasks, yet suffer from
time-consuming, excessive computational resource consumption, and unstable restoration …

Low-light image enhancement (LLIE) investigates how to improve illumination and produce
normal-light images. The majority of existing methods improve low-light images via a global …

This paper studies a diffusion-based framework to address the low-light image
enhancement problem. To harness the capabilities of diffusion models, we delve into this …

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of
an image captured in an environment with poor illumination. Recent advances in this area …