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Research Article |

Enhanced Image Inpainting in Remotely Sensed Images by Optimizing NLTV model by Ant Colony Optimization

Author(s): Manjinder Singh* and Harpreet Kaur

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 4, issue 3

Publisher : FOREX Publication

Published : 30 september 2016

e-ISSN : 2347-470X

Page(s) : 91-97

DOI: https://doi.org/10.37391/IJEER.040306

Abstract

Filling dead pixels or eliminating unwanted things is typically preferred within the applications of remotely sensed images. In proposed article, a competent image imprinting technique is demonstrated to resolve this drawback, relied nonlocal total variation. Initially remotely sensed images are effected by ill posed inverse problems i.e. image destripping, image de-noising etc. So it is required to use regularization technique to makes these problems well posed i.e. NLTV method, which is the combination of nonlocal operators and total variation model. Actually this method can make use of the good features of non-local operators for textured images and total variation method in edge preserving for color images. To optimize the proposed variation model, an Ant Colony Optimization algorithm is used in order to get similarity with the original image. And evaluate the outcomes of proposed technique with the existing technique i.e. MNLTV optimized by Bregmanized-operator-splitting algorithm which is a prediction based method. The investigation of all outcomes confirms the efficacy of this rule.

Keywords: Inpainting, Regularization, Nonlocal total variation, Multichannel nonlocal total variation, remotely sensed images.




Manjinder Singh*, Department of Electronics and Communication Engineering Chandigarh University Mohali, India; Email: manna.saini91@gmail.com

Harpreet Kaur, Department of Computer Science Chandigarh University Mohali, India; Email: harpreet8307@gmail.com

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Manjinder Singh and Harpreet Kaur (2016), Enhanced Image Inpainting in Remotely Sensed Images by Optimizing NLTV model by Ant Colony Optimization. IJEER 4(3), 91-97. DOI: 10.37391/IJEER.040306.