Name
Abstract | ||
|---|---|---|
The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressin... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2017.2753658 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Image edge detection,Image restoration,Kernel,Estimation,Computational modeling,Training data,Machine learning | Kernel (linear algebra),Computer vision,Pattern recognition,Deblurring,Computer science,Convolutional neural network,Motion blur,Filter (signal processing),Artificial intelligence,Image restoration,Deep learning,Kernel density estimation | Journal |
Volume | Issue | ISSN |
27 | 1 | 1057-7149 |
Citations | PageRank | References |
5 | 0.40 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Xu | 1 | 143 | 5.66 |
| Jin-shan Pan | 2 | 567 | 30.84 |
| Yu Jin Zhang | 3 | 1272 | 93.14 |
| Yang Ming-Hsuan | 4 | 15303 | 620.69 |