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 |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.