IEICE TRANSACTIONS on Information
No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images
Summary :
In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.3 pp.728-732
- Publication Date
- 2015/03/01
- Publicized
- 2014/12/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8175
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Hanhoon PARK
Pukyong National University
Keyword
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Hanhoon PARK, "No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 3, pp. 728-732, March 2015, doi: 10.1587/transinf.2014EDL8175.
Abstract: In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8175/_p
Copy
@ARTICLE{e98-d_3_728,
author={Hanhoon PARK, },
journal={IEICE TRANSACTIONS on Information},
title={No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images},
year={2015},
volume={E98-D},
number={3},
pages={728-732},
abstract={In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.},
keywords={},
doi={10.1587/transinf.2014EDL8175},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - No-Reference Blur Strength Estimation Based on Spectral Analysis of Blurred Images
T2 - IEICE TRANSACTIONS on Information
SP - 728
EP - 732
AU - Hanhoon PARK
PY - 2015
DO - 10.1587/transinf.2014EDL8175
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2015
AB - In this letter, we propose a new no-reference blur estimation method in the frequency domain. It is based on computing the cumulative distribution function (CDF) of the Fourier transform spectrum of the blurred image and analyzing the relationship between its shape and the blur strength. From the analysis, we propose and evaluate six curve-shaped analytic metrics for estimating blur strength. Also, we employ an SVM-based learning scheme to improve the accuracy and robustness of the proposed metrics. In our experiments on Gaussian blurred images, one of the six metrics outperformed the others and the standard deviation values between 0 and 6 could be estimated with an estimation error of 0.31 on average.
ER -
English
