Name
Title | ||
|---|---|---|
Rotation-Invariance Can Further Improve State-Of-The-Art Blind Deconvolution Techniques |
Abstract | ||
|---|---|---|
In many real-life situations, we need to reconstruct a blurred image in situations when no information about the blurring is available. This problem is known as the problem of blind deconvolution. There exist techniques for solving this problem, but these techniques are not rotation-invariant. Thus, the result of using this technique may change with rotation. So, if we rotate the image a little bit, the method, in general, leads to a different deconvolution result. Therefore, even when the original reconstruction is optimal, the reconstruction of a rotated image will be different and, thus, not optimal. To improve the quality of image decomposition, it is desirable to modify the current state-of-the art techniques by making them rotation-invariant. In this paper, we show how this can be done, and we show that this indeed improves the quality of blind deconvolution. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Iterative reconstruction,Computer vision,Invariant (physics),Blind deconvolution,Computer science,Convolution,Deconvolution,Linear programming,Artificial intelligence,Image restoration,Machine learning,Constrained optimization |
DocType | ISSN | Citations |
Conference | 1062-922X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Cervantes | 1 | 0 | 0.68 |
| Bryan Usevitch | 2 | 29 | 4.61 |
| Vladik Kreinovich | 3 | 1091 | 281.07 |