Name
Abstract | ||
|---|---|---|
Traditional image deblurring is based on deconvolution, an ill-posed problem, which is sensitive to the accuracy of the blur kernel. In this paper, we propose a blind image deblurring method based on dictionary replacing. First, we estimate the blur kernel from the blur image , and then based on the sparse representation of the image patch under over-complete dictionary, we deblur the image via replacing blur dictionary with clear dictionary. Our method avoids the deconvolution problem and can bring more high-frequency information in the deblurred image via dictionary replacing. Experimental results compared with state-of-the-art blind deblurring methods demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-31919-8_46 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
blur dictionary,deblurred image,blind image,image patch,blur kernel,traditional image deblurring,over-complete dictionary,blur image,clear dictionary,sparse representation | Kernel (linear algebra),Computer vision,K-SVD,Deblurring,Pattern recognition,Computer science,Sparse approximation,Deconvolution,Artificial intelligence | Conference |
Volume | Issue | ISSN |
7202 LNCS | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haisen Li | 1 | 49 | 5.47 |
| Yanning Zhang | 2 | 1613 | 176.32 |
| Feng Duan | 3 | 87 | 27.49 |
| Yu Zhu | 4 | 88 | 12.65 |