IEEE Trans Image Process - Single frequency inverse obstacle scattering: a sparsity constrained linear sampling method approach.

Tópicos

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Resumo

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.

Resumo Limpo

linear sampl method lsm offer qualit imag reconstruct approach known viabl altern obstacl support identif wellstudi filter backproject fbp depend linear forward scatter model practic interest imag obstacl spars apertur farfield data fix singl frequenc mode oper scenario tikhonov regular typic appli lsm produc poor imag fail captur obstacl boundari paper employ altern regular strategi base constrain sparsiti solut spatial gradient two regular approach base spatial gradient develop numer comparison fbp demonstr new method abil account aspectdepend scatter permit accur reconstruct concav obstacl wherea comparison tikhonovregular lsm demonstr propos approach signific improv obstacl recoveri sparseapertur data

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