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dc.contributor.authorZhang, Shanqing
dc.contributor.authorLi, Pengcheng
dc.contributor.authorXu, Xianghua
dc.contributor.authorLi, Li
dc.contributor.authorChang, Ching-Chun
dc.date.accessioned2020-12-11T05:11:34Z
dc.date.available2020-12-11T05:11:34Z
dc.date.issued2018
dc.identifier.issn2073-8994
dc.identifier.doi10.3390/sym10080304
dc.identifier.urihttp://hdl.handle.net/10072/400187
dc.description.abstractBlur is an important factor affecting the image quality. This paper presents an efficient no-reference (NR) image blur assessment method based on a response function of singular values. For an image, the grayscale image is computed to the acquire spatial information. In the meantime, the gradient map is computed to acquire the shape information, and the saliency map can be obtained by using scale-invariant feature transform (SIFT). Then, the grayscale image, the gradient map, and the saliency map are divided into blocks of the same size. The blocks of the gradient map are converted into discrete cosine transform (DCT) coefficients, from which the response function of singular values (RFSV) are generated. The sum of the RFSV are then utilized to characterize the image blur. The variance of the grayscale image and the DCT domain entropy of the gradient map are used to reduce the impact of the image content. The SIFT-dependent weights are calculated in the saliency map, which are assigned to the image blocks. Finally, the blur score is the normalized sum of the RFSV. Extensive experiments are conducted on four synthetic databases and two real blur databases. The experimental results indicate that the blur scores produced by our method are highly correlated with the subjective evaluations. Furthermore, the proposed method is superior to six state-of-the-art methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofpagefrom304
dc.relation.ispartofissue8
dc.relation.ispartofjournalSymmetry
dc.relation.ispartofvolume10
dc.subject.fieldofresearchApplied computing
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchcode4601
dc.subject.fieldofresearchcode460306
dc.subject.keywordsScience & Technology
dc.subject.keywordsMultidisciplinary Sciences
dc.subject.keywordsScience & Technology - Other Topics
dc.subject.keywordsimage blur assessment
dc.subject.keywordsgradient
dc.titleNo-Reference Image Blur Assessment Based on Response Function of Singular Values
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZhang, S; Li, P; Xu, X; Li, L; Chang, C-C, No-Reference Image Blur Assessment Based on Response Function of Singular Values, Symmetry, 2018, 10 (8), pp. 304
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-12-11T05:09:25Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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gro.griffith.authorZhang, Shanqing


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