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Designing of overcomplete dictionaries based on DCT and DWT

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dc.citedby9
dc.contributor.authorQayyum A.en_US
dc.contributor.authorMalik A.S.en_US
dc.contributor.authorNaufal M.en_US
dc.contributor.authorSaad M.en_US
dc.contributor.authorMazher M.en_US
dc.contributor.authorAbdullah F.en_US
dc.contributor.authorAbdullah T.A.R.B.T.en_US
dc.contributor.authorid57211138712en_US
dc.contributor.authorid12800348400en_US
dc.contributor.authorid57034338400en_US
dc.contributor.authorid56567441400en_US
dc.contributor.authorid57189096519en_US
dc.contributor.authorid57188825497en_US
dc.contributor.authorid56594684600en_US
dc.date.accessioned2023-05-29T06:12:29Z
dc.date.available2023-05-29T06:12:29Z
dc.date.issued2016
dc.descriptionAtoms; Biomedical engineering; Computer vision; Discrete wavelet transforms; Image reconstruction; Stereo image processing; Stereo vision; Students; Wavelet transforms; Computation complexity; Convergence rates; Discrete Cosine Transform(DCT); KSVD; Method of optimal directions; Over-complete dictionaries; Spare Representation; Sparse representation; Discrete cosine transformsen_US
dc.description.abstractSparse representation is very active area in computer vision and image analysis. It has many applications in de-noising, stereo vision, image painting, image restoration, image de-blurring and many. For sparse modeling, there is need to design an appropriate dictionary. However, there are many dictionaries used for sparse modeling and were reported in literature. In this paper, we implemented the fixed dictionaries and adaptive dictionaries i.e., Method of Optimal Direction (MOD) and KSVD. Both adaptive are used for training the noisy images and computing the error and recovered the number of atoms using adaptive or small patches of images. The result showed that our proposed dictionaries performed much better for atom recovery in noisy patches of the images. The dictionary based on discrete wavelet transform (DWT) basis function with KSVD produced accurate result as compared to all other dictionaries. However, for fast convergence of RMSE value to minimum, DWT with KSVD and MOD dictionaries showed higher convergence rate as compared to discrete cosine transform (DCT) with KSVD and MOD. The computation complexity increased little using the DWT dictionary as compared to DCT dictionary. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7435883
dc.identifier.doi10.1109/ISSBES.2015.7435883
dc.identifier.epage139
dc.identifier.scopus2-s2.0-84965115437
dc.identifier.spage134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84965115437&doi=10.1109%2fISSBES.2015.7435883&partnerID=40&md5=e26f09dafc7f1900732c0c0eec4c9fb9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22823
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleISSBES 2015 - IEEE Student Symposium in Biomedical Engineering and Sciences: By the Student for the Student
dc.titleDesigning of overcomplete dictionaries based on DCT and DWTen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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