Denoising based on noise parameter estimation in speckled OCT images using neural network

Mohammad R-N. Avanaki; Philippe P. Laissue; Adrian G. Podoleanu; Ali Hojjat

doi:10.1117/12.814937

30 December 2008 Denoising based on noise parameter estimation in speckled OCT images using neural network

Mohammad R-N. Avanaki, Philippe P. Laissue, Adrian G. Podoleanu, Ali Hojjat

Proceedings Volume 7139, 1st Canterbury Workshop on Optical Coherence Tomography and Adaptive Optics; 71390E (2008) https://doi.org/10.1117/12.814937
Event: 1st Canterbury Workshop and School in Optical Coherence Tomography and Adaptive Optics, 2008, Canterbury, United Kingdom

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.

Citation Download Citation

Mohammad R-N. Avanaki, Philippe P. Laissue, Adrian G. Podoleanu, and Ali Hojjat "Denoising based on noise parameter estimation in speckled OCT images using neural network", Proc. SPIE 7139, 1st Canterbury Workshop on Optical Coherence Tomography and Adaptive Optics, 71390E (30 December 2008); https://doi.org/10.1117/12.814937

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