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Images Enhancement with Brightness Preserving using MRHRBFN

by Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 7
Year of Publication: 2012
Authors: Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu
10.5120/4976-7231

Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu . Images Enhancement with Brightness Preserving using MRHRBFN. International Journal of Computer Applications. 40, 7 ( February 2012), 22-26. DOI=10.5120/4976-7231

@article{ 10.5120/4976-7231,
author = { Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu },
title = { Images Enhancement with Brightness Preserving using MRHRBFN },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 7 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number7/4976-7231/ },
doi = { 10.5120/4976-7231 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:26.378306+05:30
%A Narendra Singh Bagri
%A Sanjeev Sharma
%A Santosh Sahu
%T Images Enhancement with Brightness Preserving using MRHRBFN
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 7
%P 22-26
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the image processing, Images Enhancement with Brightness Preserving has many methods likes HE(Histogram Equalization), MHE(Multi-Histogram Equalization), IDBPHE(Image Dependent Brightness Preserving Histogram Equalization). We are proposed a novel methodology for the enhancement of images MRHRBFN (Multi-Resolution Histogram with Radial Bias Function Network). Enhancement process using pixel independent multi histogram method and Radial Bias Function Network. In process of our methodology image are decompose in terms of subbands. The sub band division perform by Curvelet transform. The Curvelet Transform divides two types of bands as higher band and lower band. The separation band of frequency generates a multiple matrix for input of radial bias Function Network. We have radial Bias function network work in low band data, because higher band data preserve brightness of image. The lower frequency matrix calculates bias and proceed weight factor when The lower value of frequency matrix regret reaches the mean value of given image. Finally we get better enhance image in comparison of multi histogram equalization.

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Index Terms

Computer Science
Information Sciences

Keywords

H.E. M.H.E. I.D.B.P.H.E. M.R.H.R.B.F.N Curvelet Transform