Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting
the proposed solutions and results. The aim of this challenge is to discover an effective …

In this paper, we propose a diffusion-based unsupervised framework that incorporates
physically explainable Retinex theory with diffusion models for low-light image …

Abstract The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level
computer vision research. The camera sensor can only capture a small amount of ambient …

Ultra-high-definition (UHD) technology has attracted widespread attention due to its
exceptional visual quality, but it also poses new challenges for low-light image …

Existing multi-modal image fusion algorithms are typically designed for high-quality images
and fail to tackle degradation (eg, low light, low resolution, and noise), which restricts image …

Diffusion models trained on large-scale datasets have achieved remarkable progress in
image synthesis. However due to the randomness in the diffusion process they often …

Images captured under low-light conditions often have various issues, such as low
brightness and contrast, and also color distortion. The aim of low-light image enhancement …

Low-light image enhancement (LLIE) is essential for numerous computer vision tasks,
including object detection, tracking, segmentation, and scene understanding. Despite …

Recent advancements in adverse weather restoration have shown potential, yet the
unpredictable and varied combinations of weather degradations in the real world pose …