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Gaussian process regression(GPR) is mostly widely used now-a-days for obtaining super resolution of an image but its applicability is limited by its computational cost when large number of examples are needed. In order to alleviate this problem a novel example learning based SR method called active-sampling Gaussian regression(AGPR).In this method in order to select more informative samples for training the regression parameters an active learning strategy is used, which shows an improvement on computational efficiency while preserving the quality of image.
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Cite Article:
"Single- Image Super-Resolution using Active -Sampling Gaussian Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 8, page no.57 - 60, August-2019, Available :http://www.ijrti.org/papers/IJRTI1908013.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator