Y Xu, Y Zhu, Y Quan, H Ji - Computer Vision and Image Understanding, 2021 - Elsevier
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 …
M Liu, Y Yu, Y Li, Z Ji, W Chen, Y Peng - Neurocomputing, 2023 - Elsevier
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 …
J Liu, W Sun, Y Su, P Jing… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …
RA Khan, Y Luo, FX Wu - IET Image Processing, 2022 - Wiley Online Library
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 …
B Zhao, W Li, W Gong - IEEE Access, 2019 - ieeexplore.ieee.org
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 …
W Zhao, Y Zhao, L Feng, J Tang - Symmetry, 2021 - mdpi.com
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 …