Abstract
The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.
Original language | English (US) |
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Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 596-605 |
Number of pages | 10 |
Volume | 2017-January |
ISBN (Electronic) | 9781538604571 |
DOIs | |
State | Published - Nov 6 2017 |
Event | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: Jul 21 2017 → Jul 26 2017 |
Other
Other | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
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Country | United States |
City | Honolulu |
Period | 7/21/17 → 7/26/17 |
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition