NDLI: A parallel approach to train FLANN for an adaptive filter

Content Provider	IEEE Xplore Digital Library
Author	Tarasia, N. Kumar Mishra, M. Dash, P.C. Kumar Samal, S.
Copyright Year	2011
Description	Author affiliation: MITM, B.P.U.T, Bhubaneswar, Orissa, India (Dash, P.C.) \|\| School of Computer Engineering, KIIT UNIVERSITY, Bhubaneswar, Orissa, India (Tarasia, N.; Kumar Mishra, M.) \|\| BRMIMIT, Pubasasan, B.P.U.T, Bhubaneswar, Orissa, India (Kumar Samal, S.)
Abstract	Images get corrupted at the time of transmission due to various noises, such as additive white Gaussian noise, impulse noise or both of these two, salt and pepper noise, multiplicative noises, random value impulse noise and many more. Neural network based image filter is one of the most important example of adaptive image filter. Adaptive neural network filter remove various types of noise such as Gaussian noise and impulsive noise. Neural networks have already been applied in several domains of image processing including image filtering. But training of those neural networks consume much time before it is actually tested on such as image filtering. Applying parallelism to image processing is increasingly practical and necessary, as our desktops are becoming multi- core machines replacing single core. Therefore, this paper presents a parallel approach called image decomposition technique to train FLANN (Functional Link Artificial Neural Network) before it is actually used for rectifying the corrupted pixels to restore the image. Experimental results obtained through SPMD (Single Program Multiple Data) simulation environment show that the proposed parallel approach to train the FLANN is feasible as it substantially reduces the training period and also make it an efficient filter to restore the image fairly well maintaining the quality of the filtered image.
Starting Page	242
Ending Page	246
File Size	941349
Page Count	5
File Format	PDF
ISBN	9781424488094
e-ISBN	9781424488100
DOI	10.1109/ICACC.2011.6016406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	SPMD salt and pepper FLANN Artificial neural networks Educational institutions Image decomposition Image restoration
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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