Components Separation in Optical Imaging with Block-structured Sparse Model
نویسندگان
چکیده
We propose a sources separation method for extracting components in highly perturbed optical imaging videos. We reconstruct the observed signal as the sum of linear representation of the components. Assuming sparsity and morphological diversity, the linear representation of each component by a well designed operator contains only a few coefficients. We regularize the separation problem with block-structured `1/`p-norm, inducing sparsity over appropriate groups of coefficients. This leads to a convex optimization problem involving complex non-smooth functionals. We show how it can be split into several simpler functionals and solved efficiently with the generalized forward-backward algorithm.
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