In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel
Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low …

Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel
is unknown. To solve this ill-posed problem, a great amount of image priors have been …

Vision Transformers have shown promising performance in image restoration, which usually
conduct window-or channel-based attention to avoid intensive computations. Although the …

In this paper, we present an effective and efficient face deblurring algorithm by exploiting
semantic cues via deep convolutional neural networks (CNNs). As face images are highly …

Blind image deblurring, ie, deblurring without knowledge of the blur kernel, is a highly ill-
posed problem. The problem can be solved in two parts: 1) estimate a blur kernel from the …

Low-rank matrix approximation (LRMA)-based methods have made a great success for
grayscale image processing. When handling color images, LRMA either restores each color …

The success of the state-of-the-art deblurring methods mainly depends on the restoration of
sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn …

Retinal fundus images are widely used for the clinical screening and diagnosis of eye
diseases. However, fundus images captured by operators with various levels of experience …