Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/16337
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dc.contributor.authorFebin I.P.
dc.contributor.authorJidesh P.
dc.contributor.authorBini A.A.
dc.date.accessioned2021-05-05T10:30:13Z-
dc.date.available2021-05-05T10:30:13Z-
dc.date.issued2020
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , Vol. 13 , , p. 941 - 949en_US
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2020.2975044
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16337-
dc.description.abstractRemotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model. © 2020 IEEE.en_US
dc.titleA Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noiseen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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