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

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images

Muhammad Shahin Uddin, Kalyan Kumar Halder, Murat Tahtali, Andrew J. Lambert, Mark R. Pickering, Margaret Marchese, and Iain Stuart
Appl. Opt. 55(31) 8905-8915 (2016)

Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter

Desmond C. Adler, Tony H. Ko, and James G. Fujimoto
Opt. Lett. 29(24) 2878-2880 (2004)

Speckle noise reduction algorithm with total variation regularization in optical coherence tomography

Guanghua Gong, Hongming Zhang, and Minyu Yao
Opt. Express 23(19) 24699-24712 (2015)

Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set

Prabakar Puvanathasan and Kostadinka Bizheva
Opt. Express 15(24) 15747-15758 (2007)

Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling

Andrew Cameron, Dorothy Lui, Ameneh Boroomand, Jeffrey Glaister, Alexander Wong, and Kostadinka Bizheva
Biomed. Opt. Express 4(9) 1769-1785 (2013)