Human activity recognition has gained importance in recent years due to its applications in
various fields such as health, security and surveillance, entertainment, and intelligent …

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 …

This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …

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 …

Numerous single image blind deblurring algorithms have been proposed to restore latent
sharp images under camera motion. However, these algorithms are mainly evaluated using …

We describe a learning-based approach to blind image deconvolution. It uses a deep
layered architecture, parts of which are borrowed from recent work on neural network …

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 …

We propose a simple yet effective L_0-regularized prior based on intensity and gradient for
text image deblurring. The proposed image prior is motivated by observing distinct …

We propose a simple yet effective L 0-regularized prior based on intensity and gradient for
text image deblurring. The proposed image prior is based on distinctive properties of text …

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 …