Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data

Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data Multimed Tools Appl https://doi.org/10.1007/s11042-018-6144-9 Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data 1 2 Nafis uddin Khan · K. V. Arya Received: 27 September 2017 / Revised: 5 May 2018 / Accepted: 15 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract De-noising of images along with the edge enhancement has always been a chal- lenging task in large scale heterogeneous image data. This paper presents a two stage image de-noising as well as edge enhancement method where in the first stage two copies of input noisy image are created through diffusion. The first copy is got by using anisotropic diffu- sion method which employ optimal diffusion function while the second copy is generated to improve the sharp edges by applying the combination of inverse heat diffusion and Canny edge detector. In the next stage, the singular value decomposition is applied on the two copies achieved in first stage to reduce the noise and improve the quality of detected edges. The optimal number of significant singular values have been estimated by the analysis of signal to noise ratio of singular value decomposed images of first copy. The singular values http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6144-9
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6144-9 Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data 1 2 Nafis uddin Khan · K. V. Arya Received: 27 September 2017 / Revised: 5 May 2018 / Accepted: 15 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract De-noising of images along with the edge enhancement has always been a chal- lenging task in large scale heterogeneous image data. This paper presents a two stage image de-noising as well as edge enhancement method where in the first stage two copies of input noisy image are created through diffusion. The first copy is got by using anisotropic diffu- sion method which employ optimal diffusion function while the second copy is generated to improve the sharp edges by applying the combination of inverse heat diffusion and Canny edge detector. In the next stage, the singular value decomposition is applied on the two copies achieved in first stage to reduce the noise and improve the quality of detected edges. The optimal number of significant singular values have been estimated by the analysis of signal to noise ratio of singular value decomposed images of first copy. The singular values

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: May 30, 2018

References

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