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 …

Offline Reinforcement Learning (RL) methods leverage previous experiences to learn better
policies than the behavior policy used for data collection. However, they face challenges …

Universal image restoration is a practical and potential computer vision task for real-world
applications. The main challenge of this task is handling the different degradation …

The removal of shadows from images is a classic problem in computer vision, aiming to
restore the lighting in shadowed areas, thereby reducing the information interference and …

Underwater image restoration is a challenging task because of water effects that increase
dramatically with distance. This is worsened by lack of ground truth data of clean scenes …

Diffusion models have recently been investigated as powerful generative solvers for image
dehazing, owing to their remarkable capability to model the data distribution. However, the …

Existing raindrop removal datasets have two shortcomings. First, they consist of images
captured by cameras with a focus on the background, leading to the presence of blurry …

The existing image-restoration methods are only effective for specific degradation tasks, but
the type of image degradation in practical applications is unknown, and mismatch between …

Image restoration aims to enhance low quality images, producing high quality images that
exhibit natural visual characteristics and fine semantic attributes. Recently, the diffusion …

Existing models for stamp removal are primarily designed for color stamps, yet lack specific
models for removing stamps from photocopied documents. In this work, the first model …