Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/12249
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dc.contributor.authorFebin, I.P.
dc.contributor.authorJidesh, P.
dc.contributor.authorBini, A.A.
dc.date.accessioned2020-03-31T08:38:52Z-
dc.date.available2020-03-31T08:38:52Z-
dc.date.issued2018
dc.identifier.citationImaging Science Journal, 2018, Vol.66, 8, pp.479-491en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/12249-
dc.description.abstractMedical, satellite or microscopic images differ in the imaging techniques used, hence their underlying noise distribution also are different. Most of the restoration methods including regularization models make prior assumptions about the noise to perform an efficient restoration. Here we propose a system that estimates and classifies the noise into different distributions by extracting the relevant features. The system provides information about the noise distribution and then it gets directed into the restoration module where an appropriate regularization method (based on the non-local framework) has been employed to provide an efficient restoration of the data. We have effectively addressed the distortion due to data-dependent noise distributions such as Poisson and Gamma along with data uncorrelated Gaussian noise. The studies have shown a 97.7% accuracy in classifying noise in the test data. Moreover, the system also shows the capability to cater to other popular noise distributions such as Rayleigh, Chi, etc. 2018, 2018 The Royal Photographic Society.en_US
dc.titleNoise classification and automatic restoration system using non-local regularization frameworksen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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