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Reseach Article

Fuzzy based Image Enhancement Method

Published on July 2015 by G.r.sinha, Neha Agrawal
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015)
Foundation of Computer Science USA
NCKITE2015 - Number 1
July 2015
Authors: G.r.sinha, Neha Agrawal

G.r.sinha, Neha Agrawal . Fuzzy based Image Enhancement Method. National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015). NCKITE2015, 1 (July 2015), 13-18.

author = { G.r.sinha, Neha Agrawal },
title = { Fuzzy based Image Enhancement Method },
journal = { National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015) },
issue_date = { July 2015 },
volume = { NCKITE2015 },
number = { 1 },
month = { July },
year = { 2015 },
issn = 0975-8887,
pages = { 13-18 },
numpages = 6,
url = { /proceedings/nckite2015/number1/21477-2645/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015)
%A G.r.sinha
%A Neha Agrawal
%T Fuzzy based Image Enhancement Method
%J National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015)
%@ 0975-8887
%N 1
%P 13-18
%D 2015
%I International Journal of Computer Applications

Though, there has been an enormous research contribution on image de-noising methods which are also called as image enhancement methods that actually enhance the desired information and suppress unwanted portion in a digital image. However, robustness is still a major challenge in this area of digital image processing. The performance has been improved by several research papers using fuzzy approaches. This work proposed a non-linear method for removing impulse noise, that is salt and pepper noise in digital grayscale images. The modified fuzzy based decision algorithm (MFBDA) is used. The noisy pixels are detected and then fuzzy based filtering works to correct the pixel. The proposed method performs better than conventional and other non-linear fuzzy based image enhancement methods. The values of statistical parameters such as PSNR (Peak signal-to-noise ratio), IEF (Image Enhancement factor), IQI (Image quality index) and SSIM (Structural similarity index) were obtained better as compared to conventional fuzzy filters.

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Index Terms

Computer Science
Information Sciences


Fuzzy Logic Image De-noising Image Enhancement Salt-and-pepper Noise.