Image motion deblurring based on deep residual shrinkage and generative adversarial networks
W Jiang, A Liu - Computational Intelligence and Neuroscience, 2022 - Wiley Online Library
A network structure (DRSN‐GAN) is proposed for image motion deblurring that combines a
deep residual shrinkage network (DRSN) with a generative adversarial network (GAN) to …
deep residual shrinkage network (DRSN) with a generative adversarial network (GAN) to …
Image motion deblurring via attention generative adversarial network
Y Zhang, T Li, Q Li, X Fu, T Kong - Computers & Graphics, 2023 - Elsevier
Image motion deblurring methods based on deep learning have achieved promising
performance. However, these methods ignore the global dependence of structural features …
performance. However, these methods ignore the global dependence of structural features …
GAN-based image deblurring using DCT loss with customized datasets
H Tomosada, T Kudo, T Fujisawa, M Ikehara - IEEE Access, 2021 - ieeexplore.ieee.org
In this paper, we propose a high quality image deblurring method that uses discrete cosine
transform (DCT) and requires less computational complexity. We train our model on a new …
transform (DCT) and requires less computational complexity. We train our model on a new …
Structure-aware motion deblurring using multi-adversarial optimized cyclegan
Recently, Convolutional Neural Networks (CNNs) have achieved great improvements in
blind image motion deblurring. However, most existing image deblurring methods require a …
blind image motion deblurring. However, most existing image deblurring methods require a …
High-frequency attention residual GAN network for blind motion deblurring
J Zhang, G Cui, J Zhao, Y Chen - IEEE Access, 2022 - ieeexplore.ieee.org
The moving image deblurring method based on deep learning has achieved good results.
However, some methods are not effective in restoring image texture detail information …
However, some methods are not effective in restoring image texture detail information …
Image deblurring with image blurring
Deep learning (DL) based methods for motion deblurring, taking advantage of large-scale
datasets and sophisticated network structures, have reported promising results. However …
datasets and sophisticated network structures, have reported promising results. However …
Deep pyramid generative adversarial network with local and nonlocal similarity features for natural motion image deblurring
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 …
deep learning. Existing methods often capture long-range dependency by a large receptive …
EDGAN: motion deblurring algorithm based on enhanced generative adversarial networks
Y Zhang, SY Ma, X Zhang, L Li, WH Ip… - The Journal of …, 2020 - Springer
Removing motion blur has been an important issue in computer vision literature. Motion blur
is caused by the relative motion between the camera and the photographed object …
is caused by the relative motion between the camera and the photographed object …
FMD-cGAN: Fast motion deblurring using conditional generative adversarial networks
In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial
Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN …
Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN …
Image blind motion deblurring method with longitudinal channel and wavelet dynamic convolution
N Jiang, Y Zhang, F Yan, X Fu, T Kong - Computers & Graphics, 2023 - Elsevier
Deep learning methods have achieved great success in image deblurring. However, these
methods also exist two limitations, the detailed information loss caused by the image …
methods also exist two limitations, the detailed information loss caused by the image …