Wavelet based despeckling of multiframe optical coherence tomography data using similarity measure and anisotropic diffusion filtering

We propose a new algorithm for despeckling multiframe Optical Coherence Tomography (OCT) data based on wavelet shrinkage using anisotropic diffusion and similarity comparison between frames. In this algorithm detail coefficients are weighted for noise reduction, where these weights are calculated based on similarity comparison between approximation coefficients. This comparison is based on the assumption that frames have similar structural content while noise is temporally uncorrelated. Approximation coefficients are denoised using Perona Malik anisotropic diffusion. Finally these processed coefficients are averaged to get a denoised image. Experimental results show that the proposed method performs better than the previously formulated denoising algorithms both in terms of noise reduction and structural content preservation.