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 …

Image motion deblurring methods based on deep learning have achieved promising
performance. However, these methods ignore the global dependence of structural features …

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 …

Recently, Convolutional Neural Networks (CNNs) have achieved great improvements in
blind image motion deblurring. However, most existing image deblurring methods require a …

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 …

Deep learning (DL) based methods for motion deblurring, taking advantage of large-scale
datasets and sophisticated network structures, have reported promising results. However …

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 …

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 …

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 …

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 …