This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …

We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-
LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel …

Images captured under incorrect exposures unavoidably suffer from mixed degradations of
lightness and structures. Most existing deep learning-based exposure correction methods …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

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 …

Shadow removal, which aims to restore the background in the shadow regions, is
challenging due to the highly ill-posed nature. Most existing deep learning-based methods …

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 …

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

Image harmonization, aiming to make composite images look more realistic, is an important
and challenging task. The composite, synthesized by combining foreground from one image …

The lighting environment in the real world is so complex that most existing low-light image
restoration methods suffer from color cast and local over-exposure. In order to solve these …