Neural networks (NNs) are becoming the tool of choice for sharpening blurred images. We
discuss and categorize deblurring NNs. Then we evaluate seven NNs for non-blind …

Non-uniform blind motion deblurring is a challenging yet important problem in image
processing that receives enduring attention in the last decade. The non-uniformity nature of …

Non-uniform blind deblurring of dynamic scenes has always been a challenging problem in
image processing because of the diverse of blurring sources. Traditional methods based on …

Single image deblurring, aiming at recovering a latent sharp image from a blurry image, is a
highly ill-posed task as there exist infinite feasible solutions. One successful practice of the …

There is a growing demand for monitors to provide high-quality visualization with more bits
representing each rendered pixel. However, since most existing images and videos are of …

Processing images with heterogeneous blur remains challenging due to multiple
degradation aspects that could affect structural properties. This study proposes a deep …

Conventional deconvolution methods utilize hand-crafted image priors to constrain the
optimization. While deep-learning-based methods have simplified the optimization by end-to …

For aeroengine multitask prognosis, dataset's quantity and quality significantly affect the
prediction performance. Due to the insufficiency and high redundancy of collected data, data …

It is of great importance to capture long-range dependency in image deblurring based on
deep learning. Existing methods often capture long-range dependency by a large receptive …

The existing dehazing algorithms are problematic because of dense haze being unevenly
distributed on the images, and the deep convolutional dehazing network relying too greatly …