This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building
on the last year edition the current challenge was organized in two tracks with a track …

Global modeling-based image restoration frameworks have become popular. However, they
often require a high memory footprint and do not consider task-specific degradation. Our …

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 …

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

Image restoration aims to reconstruct a sharp image from its degraded counterpart, which
plays an important role in many fields. Recently, Transformer models have achieved …

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 …

Deep neural networks have achieved remarkable progress in enhancing low-light images
by improving their brightness and eliminating noise. However, most existing methods …

Underwater image quality is seriously degraded due to the insufficient light in water.
Although artificial illumination can assist imaging, it often brings non-uniform illumination …

Low-light image enhancement is an effective solution for improving image recognition by
both humans and machines. Due to low illuminance, images captured in such conditions …

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Besides dealing with this long-standing task in the spatial domain, a few approaches seek …