Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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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Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 100
Country: India
Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision
Authors: (PhD/Dr. count: 0)
View Count (all-time): 265
Total Views (Real + Logic): 21342
Total Downloads (simulated): 11044
Publish Date: 1970 01, Thu
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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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