NDLI: Filtering of mixed Gaussian and impulsive noise using morphological contrast detectors

Content Provider	IET Digital Library
Author	Santibañez, Jorge D. Mendiola Villalobos, Iván R. Terol
Abstract	Several morphological transformations to detect noise are introduced. The initial method is a modification of a procedure presented previously in the current literature. The proposals given in the study allow to detect noise in two ways: (i) using a contrast measure and (ii) applying different proximity criteria into several proposed toggle mappings. In the end, two of the proposals given in this study yield a better performance with respect to methods in which this research is based. However, although the methodology to identify noise works adequately, the results are limited due to the use of the structuring element. In Section 4, an image with two types of noise is cleaned. Such image is contaminated with zero mean Gaussian noise with 0.01 variance and 5% of salt and pepper noise. From this experiment, the proposal giving the best performance is selected; subsequently, this is compared with other recent operators as PDEs, wavelets, morphological connected rank max opening and amoebas.
Starting Page	131
Ending Page	141
Page Count	11
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 3, Mar (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0615
Journal	IET Image Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2013-11-04
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Amoebas Computer Vision And Image Processing Technique Contrast Measure Filtering Method Filtering Method in Signal Processing Filtering Theory Image Denoising Image Processing Impulse Noise Morphological Connected Rank Max Opening Morphological Contrast Detector Noise Detection Optical, Image And Video Signal Processing PDE Pepper Noise Proximity Criteria Salt Noise Structuring Element Toggle Mappings Wavelets Zero Mean Gaussian Noise
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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