Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes

S. Alireza Golestaneh; Lina Karam

doi:10.1109/CVPR.2017.71

Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes

S. Alireza Golestaneh, Lina Karam

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

34 Scopus citations

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)
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	https://doi.org/10.1109/CVPR.2017.71
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
Country	United States
City	Honolulu
Period	7/21/17 → 7/26/17

ASJC Scopus subject areas

Signal Processing
Computer Vision and Pattern Recognition

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Alireza Golestaneh, S., & Karam, L. (2017). Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (Vol. 2017-January, pp. 596-605). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2017.71

Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes. / Alireza Golestaneh, S.; Karam, Lina.

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 596-605.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Alireza Golestaneh, S & Karam, L 2017, Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 596-605, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, United States, 7/21/17. https://doi.org/10.1109/CVPR.2017.71

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AB - 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.

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Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes

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