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

44 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
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

Publication series

Name	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Volume	2017-January

Other

Other	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Country/Territory	United States
City	Honolulu
Period	7/21/17 → 7/26/17

ASJC Scopus subject areas

Signal Processing
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2017.71

Cite this

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

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. 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

Alireza Golestaneh S, Karam L. 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. Institute of Electrical and Electronics Engineers Inc. 2017. p. 596-605. (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017). https://doi.org/10.1109/CVPR.2017.71

Alireza Golestaneh, S. ; Karam, Lina. / Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 596-605 (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017).

@inproceedings{1e89ee3f6672478ab86d581f239854e5,

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

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.",

author = "{Alireza Golestaneh}, S. and Lina Karam",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

year = "2017",

month = nov,

day = "6",

doi = "10.1109/CVPR.2017.71",

language = "English (US)",

series = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "596--605",

booktitle = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",

}

TY - GEN

T1 - Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes

AU - Alireza Golestaneh, S.

AU - Karam, Lina

PY - 2017/11/6

Y1 - 2017/11/6

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85044527408&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044527408&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2017.71

DO - 10.1109/CVPR.2017.71

M3 - Conference contribution

AN - SCOPUS:85044527408

T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

SP - 596

EP - 605

BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Y2 - 21 July 2017 through 26 July 2017

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this