Name
Abstract | ||
|---|---|---|
State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as m... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2017.2761348 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Kernel,Cameras,Optical imaging,Motion segmentation,Dynamics,Adaptive optics,Estimation | Kernel (linear algebra),Computer vision,Shake,Pattern recognition,Deblurring,Computer science,Gaussian blur,Optical flow estimation,Artificial intelligence,Optical imaging,Adaptive optics | Journal |
Volume | Issue | ISSN |
40 | 10 | 0162-8828 |
Citations | PageRank | References |
5 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Hyun Kim | 1 | 359 | 29.05 |
| Seungjun Nah | 2 | 406 | 12.44 |
| Kyoung Mu Lee | 3 | 3228 | 153.84 |