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Poisson Reducing Unilateral Filtering for X-ray Image Denoising

Published on July 2016 by Kirti V.thakur, Omkar H.damodare, Ashok M. Sapkal
journal_cover_thumbnail

International Conference on Communication Computing and Virtualization
Foundation of Computer Science USA
ICCCV2016 - Number 2
July 2016
Authors: Kirti V.thakur, Omkar H.damodare, Ashok M. Sapkal
05cb018f-b0e5-4396-9fe7-8f214bbcf372

Kirti V.thakur, Omkar H.damodare, Ashok M. Sapkal . Poisson Reducing Unilateral Filtering for X-ray Image Denoising. International Conference on Communication Computing and Virtualization. ICCCV2016, 2 (July 2016), 0-0.

@article{
author = { Kirti V.thakur, Omkar H.damodare, Ashok M. Sapkal },
title = { Poisson Reducing Unilateral Filtering for X-ray Image Denoising },
journal = { International Conference on Communication Computing and Virtualization },
issue_date = { July 2016 },
volume = { ICCCV2016 },
number = { 2 },
month = { July },
year = { 2016 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icccv2016/number2/918-1662/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication Computing and Virtualization
%A Kirti V.thakur
%A Omkar H.damodare
%A Ashok M. Sapkal
%T Poisson Reducing Unilateral Filtering for X-ray Image Denoising
%J International Conference on Communication Computing and Virtualization
%@ 2249-0868
%V ICCCV2016
%N 2
%P 0-0
%D 2016
%I International Journal of Applied Information Systems
Abstract

This paper is enhancement of author's earlier work, Poisson noise Reducing Bilateral Filter (PRBF). This paper recommends two major changes in PRBF. One change is to make PRBF independent of distance variance i. e. filter performance is based on single parameter (range variance). Therefore this proposed work is named as Poisson Reducing UnilateralFiltering (PRUF). Similarly, performance of PRBF on edge region is enhanced due to second change and same is demonstrated through experimentation. Peak signal to noise ratio (PSNR) and Structural similarity index matching (SSIM) quality metrics are used for comparison of proposed PRUF with existing PRBF. performance is based on single parameter (range variance). Therefore this proposed work is named as Poisson Reducing UnilateralFiltering (PRUF). Similarly, performance of PRBF on edge region is enhanced due to second change and same is demonstrated through experimentation. Peak signal to noise ratio (PSNR) and Structural similarity index matching (SSIM) quality metrics are used for comparison of proposed PRUF with existing PRBF.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Poisson noise X-ray denoising Bilateral filter Poisson noise reducing bilateral filter PSNR SSIM.