Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/16292
Title: A Perceptually Inspired Variational Model for Enhancing and Restoring Remote Sensing Images
Authors: Jidesh P.
Febin I.P.
Issue Date: 2021
Citation: IEEE Geoscience and Remote Sensing Letters , Vol. 18 , 2 , p. 251 - 255
Abstract: Perceptually inspired algorithms have captured the recent attention of scientists and engineers due to their inherent capability to enhance the contrast of images, especially from the remote sensing domain. In this letter, we propose a perceptually inspired retinex model relying on the variational framework for enhancing and denoising satellite images captured by various imaging devices. A variational framework incorporates priors and data fidelity aspects in the designed functional, whose optimized solution yields the desired output. The model respects the distribution of the noise while enhancing the data. The overall performance is demonstrated using the visual and quantitative measures. © 2004-2012 IEEE.
URI: https://doi.org/10.1109/LGRS.2020.2969411
http://idr.nitk.ac.in/jspui/handle/123456789/16292
Appears in Collections:1. Journal Articles

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