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 …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Images taken in dynamic scenes may contain unwanted motion blur, which significantly
degrades visual quality. Such blur causes short-and long-range region-specific smoothing …

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …

Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …

While deep neural networks (DNN) based single image super-resolution (SISR) methods
are rapidly gaining popularity, they are mainly designed for the widely-used bicubic …

Dynamic Scene deblurring is a challenging low-level vision task where spatially variant blur
is caused by many factors, eg, camera shake and object motion. Recent study has made …

In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …

We present a simple and effective blind image deblurring method based on the dark
channel prior. Our work is inspired by the interesting observation that the dark channel of …