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

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …

Image Deblurring is a very popular area of research in all over the world. It is an illposed
problem which still does not have an ideal solution. Therefore, in order to analyse the …

In this paper, we study design of deep neural networks for tasks of image restoration. We
propose a novel style of residual connections dubbed" dual residual connection", which …

Blind image deblurring remains a topic of enduring interest. Learning based approaches,
especially those that employ neural networks have emerged to complement traditional …

Deblurring an image has been a long researched problem. This problem is very complex
due to the lack of sufficient information about the blur parameters. Image deblurring is …

We propose a neural approach for fusing an arbitrary-length burst of photographs suffering
from severe camera shake and noise into a sharp and noise-free image. Our novel …

We propose a deep learning approach to remove motion blur from a single image captured
in the wild, ie, in an uncontrolled setting. Thus, we consider motion blur degradations that …

This paper presents a robust algorithm for estimating a single latent sharp image given
multiple blurry and/or noisy observations. The underlying multi-image blind deconvolution …

Following their success in visual recognition tasks, Vision Transformers (ViTs) are being
increasingly employed for image restoration. As a few recent works claim that ViTs for image …