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 …

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 …

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 …

Enhancing images in low-light scenes is a challenging but widely concerned task in the
computer vision. The mainstream learning-based methods mainly acquire the enhanced …

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 …

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 enhancement (LLE) is a fundamental technique for improving the visual
perception and interpretability of images and videos that suffer from low light degradation. In …

We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that
decouples the traditional single denoising diffusion process into residual diffusion and noise …

Recently, many video enhancement methods have been proposed to improve video quality
from different aspects such as color, brightness, contrast, and stability. Therefore, how to …

The images captured under improper exposure conditions often suffer from contrast
degradation and detail distortion. Contrast degradation will destroy the statistical properties …