.png){kind=icon}
.png){kind=icon}
Return
Structure-Aware Motion Deblurring Using Multi-Adversarial Optimized CycleGAN
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2021.3092814.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, Convolutional Neural Networks (CNNs) have achieved great improvements in blind image motion deblurring. However, most existing image deblurring methods require a large amount of paired training data and fail to maintain satisfactory structural information, which greatly limits their application scope. In this paper, we present an unsupervised image deblurring method based on a multi-adversarial optimized cycle-consistent generative adversarial network (CycleGAN). Although original CycleGAN can handle unpaired training data well, the generated high-resolution images are probable to lose content and structure information. To solve this problem, we utilize a multi-adversarial mechanism based on CycleGAN for blind motion deblurring to generate high-resolution images iteratively. In this multi-adversarial manner, the hidden layers of the generator are gradually supervised, and the implicit refinement is carried out to generate high-resolution images continuously. Meanwhile, we also introduce the structure-aware mechanism to enhance the structure and detail retention ability of the multi-adversarial network for deblurring by taking the edge map as guidance information and adding multi-scale edge constraint functions. Our approach not only avoids the strict need for paired training data and the errors caused by blur kernel estimation, but also maintains the structural information better with multi-adversarial learning and structure-aware mechanism. Comprehensive experiments on several benchmarks have shown that our approach prevails the state-of-the-art methods for blind image motion deblurring.
Keywords:
Image edge detection
Kernel
Image restoration
Estimation
Training data
Generative adversarial networks
Computer architecture
Unsupervised image deblurring
multi-adversarial
structure-aware
edge refinement
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
View more organizations