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Speckle reduction using an artificial neural network algorithm

Avanaki, M.R.N., Laissue, P.P., Eom, T.J., Podoleanu, Adrian G.H., Hojjatoleslami, A. (2013) Speckle reduction using an artificial neural network algorithm. Applied Optics, 52 (21). pp. 5050-5057. ISSN 1559-128X. (doi:10.1364/AO.52.005050) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1364/AO.52.005050

Abstract

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images. © 2013 Optical Society of America.

Item Type: Article
DOI/Identification number: 10.1364/AO.52.005050
Uncontrolled keywords: Neural networks, Optical tomography, Speckle, Artificial neural network algorithm, Contrast to noise ratio, Noise parameters, Processed images, Rayleigh distributions, Rayleigh functions, Speckle reduction, Wavelet features, Algorithms, algorithm, animal, artificial neural network, computer assisted diagnosis, Drosophila, equipment design, image processing, larva, optical coherence tomography, physiology, procedures, signal noise ratio, theoretical model, Algorithms, Animals, Drosophila, Equipment Design, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Larva, Models, Theoretical, Neural Networks (Computer), Signal-To-Noise Ratio, Tomography, Optical Coherence
Subjects: Q Science > QC Physics
R Medicine > R Medicine (General) > R857.O6 Optical coherence tomography
Divisions: Faculties > Sciences > School of Physical Sciences > Applied Optics Group
Depositing User: Giles Tarver
Date Deposited: 16 Jul 2015 08:51 UTC
Last Modified: 29 May 2019 14:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49333 (The current URI for this page, for reference purposes)
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