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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 21 — Jul. 20, 2013
  • pp: 5050–5057

Speckle reduction using an artificial neural network algorithm

Mohammad R. N. Avanaki, P. Philippe Laissue, Tae Joong Eom, Adrian G. Podoleanu, and Ali Hojjatoleslami  »View Author Affiliations

Applied Optics, Vol. 52, Issue 21, pp. 5050-5057 (2013)

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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

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(110.3000) Imaging systems : Image quality assessment
(170.1650) Medical optics and biotechnology : Coherence imaging

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: June 1, 2013
Revised Manuscript: June 1, 2013
Manuscript Accepted: June 14, 2013
Published: July 11, 2013

Virtual Issues
Vol. 8, Iss. 8 Virtual Journal for Biomedical Optics

Mohammad R. N. Avanaki, P. Philippe Laissue, Tae Joong Eom, Adrian G. Podoleanu, and Ali Hojjatoleslami, "Speckle reduction using an artificial neural network algorithm," Appl. Opt. 52, 5050-5057 (2013)

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