Name
Abstract | ||
|---|---|---|
The motion blurred image is caused by the relative motion between the target and the capturing device during the exposure time. It's difficult to analyze the face information of the motion blurred face image, therefore motion deblurring is needed. However, the existing algorithms cannot deal with the diversity of motion blur kernels well. Based on that, this paper proposes an iterative spiral optimization algorithm for blind motion blurring. The algorithm makes the blurred image spirally approximate the sharp image by calling the deblurring generator multiple times. It is proved that the algorithm can effectively restore the motion blurred image with diverse blurred kernels in the approximate natural state, and improve the visual effect of the image. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-31456-9_18 | BIOMETRIC RECOGNITION (CCBR 2019) |
Keywords | Field | DocType |
Iterative spiral optimization,Generative Adversarial Network,Motion blur,Image blind restoration | Computer vision,Spiral,Generative adversarial network,Deblurring,Computer science,Relative motion,Motion blur,Artificial intelligence,Optimization algorithm | Conference |
Volume | ISSN | Citations |
11818 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |