INNOVATIVE TECHNIQUE FOR RESTORATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGES AND COMPARISON WITH BM3D

The automated processing of Optical Coherence tomography images has main difficulty of presence of noise which varies over the entire image. We first segment the OCT image into two regions, one containing different layers of eye and the other which contains only noise. The similar rows which contain only noise are considered to model the noise and then we find the local threshold value in adaptive manner. We were formulating a novel algorithm for OCT image restoration which uses a threshold derived from mean and variance of discrete wavelet coefficients in different sub bands of noisy image (segment of OCT image containing only noise). The found threshold will be different for the two segments of the image and all their sub bands. Thus this algorithm won't smooth the entire image and preserves the edge information and removes the noise. We compared our results with one of the most efficient technique BM3D.