Improved Image Denoising Filter using Low Rank & Total Variation

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Garima Goyal
Garima Goyal
1 Jyothy Institute of Technology/ Visvervaraya Technological University

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Better diagnosis of disease is possible only with the better microscopic images. To do so images of the affected area are captured and then noise is removed to obtain accurate diagnosis. Many algorithms have been proposed till date. But they are capable of removing noise only in spatial domains so this paper tries to overcome that by combining low rank filter and regularization. If we only reduce noise in spatial or spectral domain, artefacts or distortions will be introduced in other domains. At the same time, this kind of methods will destroy the correlation in spatial or spectral domain. Spatial and spectral information should be considered jointly to remove the noise efficiently. Low rank algorithms are good as they encloses semantic information as well as poses strong identification capability.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Garima Goyal. 1970. \u201cImproved Image Denoising Filter using Low Rank & Total Variation\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 16 (GJCST Volume 16 Issue F1): .

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GJCST Volume 16 Issue F1
Pg. 13- 15
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-F Classification: H.2.8 I.3.3
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v1.2

Issue date

April 20, 2016

Language

English

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Better diagnosis of disease is possible only with the better microscopic images. To do so images of the affected area are captured and then noise is removed to obtain accurate diagnosis. Many algorithms have been proposed till date. But they are capable of removing noise only in spatial domains so this paper tries to overcome that by combining low rank filter and regularization. If we only reduce noise in spatial or spectral domain, artefacts or distortions will be introduced in other domains. At the same time, this kind of methods will destroy the correlation in spatial or spectral domain. Spatial and spectral information should be considered jointly to remove the noise efficiently. Low rank algorithms are good as they encloses semantic information as well as poses strong identification capability.

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Improved Image Denoising Filter using Low Rank & Total Variation

Garima Goyal
Garima Goyal Jyothy Institute of Technology/ Visvervaraya Technological University

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