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A no-reference image sharpness estimation based on expectation of wavelet transform coefficients
Hengjun Zhao1,3; Bin Fang1; Yuan Yan Tang2
2013-12-01
Conference Name2013 IEEE International Conference on Image Processing
Source Publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages374-378
Conference Date15-18 Sept. 2013
Conference PlaceMelbourne, VIC
CountryAustralia
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

In this work, the expectation of wavelet transform coefficients is used for estimating an image sharpness. It's based on the observation that the greater the probability of big detail coefficients, the more pixels appear sharply, and consequently, the sharper the image. Specifically, an input image is firstly decomposed into three directional sub-bands by a separable discrete wavelet transform. Then these directional sub-bands are viewed as three random variables, and their expectations are computed. Finally, The proposed sharpness index is the weighted sum of three expectations. The experiments show that, despite its simplicity, the proposed sharpness index is competitive with the current best-performance techniques for no-reference image sharpness estimation. 

KeywordExpectation Image Sharpness Metric Wavelet Decomposition
DOIhttps://doi.org/10.1109/ICIP.2013.6738077
URLView the original
Indexed BySCI
Language英语
WOS Research AreaImaging Science & Photographic Technology
WOS SubjectImaging Science & Photographic Technology
WOS IDWOS:000351597600076
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被引频次[WOS]:2   [WOS记录]     [WOS相关记录]
Document TypeConference paper
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Chongqing University, College of Computer and Science, Chongqing, China, 400030
2.Macau University, Department of Computer and Information, Macau, China, 999078
3.Chongqing University of Arts and Sciences, Department of Mathematics & KLDAIP, China, 402160
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Hengjun Zhao,Bin Fang,Yuan Yan Tang. A no-reference image sharpness estimation based on expectation of wavelet transform coefficients[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2013:374-378.
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