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 …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic map**s from low-light to normally …

Nighttime image dehazing is a challenging task due to the presence of multiple types of
adverse degrading effects including glow, haze, blur, noise, color distortion, and so on …

Exposure correction task aims to correct the underexposure and its adverse overexposure
images to the normal exposure in a single network. As well recognized, the optimization flow …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

In the real world, image degradations caused by rain often exhibit a combination of rain
streaks and raindrops, thereby increasing the challenges of recovering the underlying clean …

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …

Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep
unfolding framework by formulating the problem into a data module and a prior module …

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

Lightness adaptation is vital to the success of image processing to avoid unexpected visual
deterioration, which covers multiple aspects, eg, low-light image enhancement, image …