1. Low Tensor Train- and Low Multilinear Rank Approximationsfor De-speckling and Compression of 3D Optical Coherence Tomography Images

    Low Tensor Train- and Low Multilinear Rank Approximationsfor De-speckling and Compression of 3D Optical Coherence Tomography Images

    This paper proposes low tensor-train (TT) rank and low multilinear (ML) rank approximations for de-speckling and compression of 3D optical coherence tomography (OCT) images for a given compression ratio (CR). To this end, we derive the alternating direction method of multipliers based algorithms for the related problems constrained with the low TT- and low ML rank. Rank constraints are implemented through the Schatten-p (Sp) norm, p e {0, 1/2, 2/3, 1}, of unfolded matrices. We provide the proofs of global convergence towards a stationary point for both algorithms. Rank adjusted 3D OCT image tensors are finally approximated through ...

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