Title
Blind Multi-Frame Super Resolution With Non-Identical Blur
Abstract
Real world video super resolution is an challenging problem due to the complex motion field and unknown blur kernel. Although multi-frame super resolution has been extensively studied in past decades, it still remained problems and always assumed that the blur kernels were identical in different frames. In this paper, we propose an novel blind multi-frame super resolution method with non-identical blur. To estimate blur kernels of different frames, we propose using salient edges selection method for more accurate kernel estimation. The whole process of estimation is based on Hyper-Laplacian prior, and iterative value updating through a multi-scale process. After the kernels of different frames are estimated, the high resolution frame is reconstructed using a cost function. The proposed method can obtain superior results, and outperforms the state of the art in the experiments through subjective and objective evaluation.
Year
DOI
Venue
2017
10.1007/978-3-319-67777-4_43
INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017
Keywords
Field
DocType
Multi-frame super resolution, Non-identical kernel, Blind estimation, Salient edges selection
Kernel (linear algebra),Computer vision,Motion field,Computer science,Artificial intelligence,Superresolution,Salient,Kernel density estimation
Conference
Volume
ISSN
Citations 
10559
0302-9743
0
PageRank 
References 
Authors
0.34
14
6
Name
Order
Citations
PageRank
Wei Sun17229.00
Jinqiu Sun2338.27
Xueling Chen301.69
Yu Zhu48812.65
Haisen Li5495.47
Yanning Zhang61613176.32