Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/12274
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dc.contributor.authorYamanappa, W.-
dc.contributor.authorSudeep, P.V.-
dc.contributor.authorSabu, M.K.-
dc.contributor.authorRajan, J.-
dc.date.accessioned2020-03-31T08:38:54Z-
dc.date.available2020-03-31T08:38:54Z-
dc.date.issued2018-
dc.identifier.citationIEEE Access, 2018, Vol.6, , pp.66914-66922en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/12274-
dc.description.abstractMost of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method. 2013 IEEE.en_US
dc.titleNon-local means image denoising using shapiro-wilk similarity measureen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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