Denoising Based on Noise Parameter Estimation in Speckled OCT Images Using Neural Network

Avanaki, M.R.N. and Laissue, P.P. and Podoleanu, A.G.H. and Hojjatoleslami, A. (2008) Denoising Based on Noise Parameter Estimation in Speckled OCT Images Using Neural Network. In: Podoleanu, A.G.H., ed. 1st Canterbury Workshop on Optical Coherence Tomography and Adaptive Optics. SPIE 71390E-71390E. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1117/12.814937

Abstract

This paper presents a neural network based technique to denoise speckled images in optical coherence tomography (OCT). Speckle noise is modeled as Rayleigh distribution, and the neural network estimates the noise parameter, sigma. Twenty features from each image are used as input for training the neural network, and the sigma value is the single output of the network. The certainty of the trained network was more than 91 percent. The promising image results were assessed with three No-Reference metrics, with the Signal-to-Noise ratio of the denoised image being considerably increased.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Optical Coherence Tomography (OCT), Neural Network, Speckle Noise Reduction, No-Reference (NR) metrics
Subjects: Q Science
T Technology
R Medicine
Divisions: Faculties > Science Technology and Medical Studies > School of Biosciences
Faculties > Science Technology and Medical Studies > School of Physical Sciences
Depositing User: Sayed Ali Hojjatoleslami
Date Deposited: 01 Oct 2009 07:47
Last Modified: 28 Aug 2012 11:36
Resource URI: http://kar.kent.ac.uk/id/eprint/22678 (The current URI for this page, for reference purposes)
  • Depositors only (login required):