IEEE Trans Image Process - Compressive blind image deconvolution.

Tópicos

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Resumo

We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with is employed as a regularization term for the image, while a simultaneous auto-regressive regularization term is selected for the blur. Nevertheless, the proposed approach is very general and it can be easily adapted to other state-of-the-art BID schemes that utilize different, application specific, image/blur regularization terms. Experimental results, obtained with simulations using blurred synthetic images and real passive millimeter-wave images, show the feasibility of the proposed method and its advantages over existing approaches.

Resumo Limpo

propos novel blind imag deconvolut bid regular framework compress sens cs base imag system captur blur imag propos framework reli constrain optim techniqu solv sequenc unconstrain subproblem allow incorpor exist cs reconstruct algorithm compress bid problem exampl nonconvex lp quasinorm employ regular term imag simultan autoregress regular term select blur nevertheless propos approach general can easili adapt stateoftheart bid scheme util differ applic specif imageblur regular term experiment result obtain simul use blur synthet imag real passiv millimeterwav imag show feasibl propos method advantag exist approach

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