Created by W.Langdon from gp-bibliography.bib Revision:1.4020

@Article{Kadu:2013:IJARCET, author = "Shweta R. Kadu and A. D. Gawande and L. K Gautam", title = "Blind Image De-convolution In Surveillance Systems By Genetic Programming", journal = "International Journal of Advanced Research in Computer Engineering \& Technology", year = "2013", volume = "2", number = "4", pages = "1415--1419", month = apr, keywords = "genetic algorithms, genetic programming, image blind de-convolution, maximum likelihood, PSF", ISSN = "22781323", URL = "http://ijarcet.org/?p=338", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:e04f9e103f8d2c09e8f86bd16ad4ca73", URL = "http://ijarcet.org/wp-content/uploads/IJARCET-VOL-2-ISSUE-4-1415-1419.pdf", size = "5 pages", abstract = "surveillance systems has an important part as image acquisition and filtering, segmentation, object detection and tracking the object in that image. In blind image de-convolution .most of the methods requires that the PSF and the original image must be irreducible. Blurring is a perturbation due to the imaging system while noise is intrinsic to the detection process. Therefore image de-convolution is basically a post-processing of the detected images aimed to reduce the disturbing effects of blurring and noise. Image de-convolution implies the solution of a linear equation ,but this problem turns out to be ill-posed: the solution may not exist or may not be unique. Moreover, even if a unique solution can be found this solution is strongly perturbed by noise propagation.In this papers we proposed a genetic programming based blind-image de-convolution Blind De-convolution algorithm can be used effectively when of distortion is known. It restores image and Point Spread Function (PSF) simultaneously. This algorithm can be achieved based on Maximum Likelihood Estimation (MLE).", notes = "Shri Pannalal Research Institute of Technology. PDF gives date as Jan 2013", }

Genetic Programming entries for Shweta R Kadu A D Gawande L K Gautam