IEEE Trans Image Process - Fast transforms for acoustic imaging--part I: theory.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ method(1969) cluster(1462) data(1082) }
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{ state(1844) use(1261) util(961) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
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Resumo

The classical approach for acoustic imaging consists of beamforming, and produces the source distribution of interest convolved with the array point spread function. This convolution smears the image of interest, significantly reducing its effective resolution. Deconvolution methods have been proposed to enhance acoustic images and have produced significant improvements. Other proposals involve covariance fitting techniques, which avoid deconvolution altogether. However, in their traditional presentation, these enhanced reconstruction methods have very high computational costs, mostly because they have no means of efficiently transforming back and forth between a hypothetical image and the measured data. In this paper, we propose the Kronecker Array Transform (KAT), a fast separable transform for array imaging applications. Under the assumption of a separable array, it enables the acceleration of imaging techniques by several orders of magnitude with respect to the fastest previously available methods, and enables the use of state-of-the-art regularized least-squares solvers. Using the KAT, one can reconstruct images with higher resolutions than was previously possible and use more accurate reconstruction techniques, opening new and exciting possibilities for acoustic imaging.

Resumo Limpo

classic approach acoust imag consist beamform produc sourc distribut interest convolv array point spread function convolut smear imag interest signific reduc effect resolut deconvolut method propos enhanc acoust imag produc signific improv propos involv covari fit techniqu avoid deconvolut altogeth howev tradit present enhanc reconstruct method high comput cost most mean effici transform back forth hypothet imag measur data paper propos kroneck array transform kat fast separ transform array imag applic assumpt separ array enabl acceler imag techniqu sever order magnitud respect fastest previous avail method enabl use stateoftheart regular leastsquar solver use kat one can reconstruct imag higher resolut previous possibl use accur reconstruct techniqu open new excit possibl acoust imag

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