Name
Abstract | ||
|---|---|---|
In this work, we investigate the relation between the edge profiles present in a motion blurred image and the underlying camera motion responsible for causing the motion blur. While related works on camera motion estimation (CME) rely on the strong assumption of space-invariant blur, we handle the challenging case of general camera motion. We first show how edge profiles alone can be harnessed to perform direct CME from a single observation. While it is routine for conventional methods to jointly estimate the latent image too through alternating minimization, our above scheme is best-suited when such a pursuit is either impractical or inefficacious. For applications that actually favor an alternating minimization strategy, the edge profiles can serve as a valuable cue. We incorporate a suitably derived constraint from edge profiles into an existing blind deblurring framework and demonstrate improved restoration performance. Experiments reveal that this approach yields state-of-the-art results for the blind deblurring problem. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPR.2017.67 | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | Field | DocType |
camera motion estimation,space-invariant blur,motion blurred image,motion blur,camera motion | Computer vision,Motion field,Pattern recognition,Deblurring,Latent image,Computer science,Motion blur,Minification,Artificial intelligence,Motion estimation | Conference |
Volume | Issue | ISSN |
2017 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-5386-0458-8 | 2 | 0.36 |
References | Authors | |
29 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subeesh Vasu | 1 | 4 | 2.42 |
| A. N. Rajagopalan | 2 | 1106 | 92.02 |