speckle noise reduction in optical coherence tomography using twodimensional curvelet-based dictionary learning

The process of interpretation of high-speed optical coherence tomography (oct) images is restricted due to the large speckle noise. to address this problem, this paper proposes a new method using two-dimensional (2d) curvelet-based k-svd algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2d spectral-domain oct images. for this purpose, we take curvelet transform of the noisy image. in the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on k-svd dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients’ matrix in each sub-band. we also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. we demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available oct b-scans with and without non-neovascular age-related macular degeneration (amd), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.