IEEE Trans Image Process - Semi-blind sparse image reconstruction with application to MRFM.

Tópicos

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{ model(3404) distribut(989) bayesian(671) }
{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ system(1976) rule(880) can(841) }
{ activ(1138) subject(705) human(624) }
{ framework(1458) process(801) describ(734) }
{ model(3480) simul(1196) paramet(876) }
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{ method(1969) cluster(1462) data(1082) }
{ imag(1057) registr(996) error(939) }
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{ system(1050) medic(1026) inform(1018) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
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{ estim(2440) model(1874) function(577) }
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{ measur(2081) correl(1212) valu(896) }
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{ imag(2675) segment(2577) method(1081) }
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{ take(945) account(800) differ(722) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
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{ record(1888) medic(1808) patient(1693) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
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{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

Resumo Limpo

propos solut imag deconvolut problem convolut kernel point spread function psf assum partial known small perturb generat model exploit produc princip compon explain psf uncertainti highdimension space unlik recent develop blind deconvolut natur imag assum imag spars pixel basi natur sparsiti aris magnet reson forc microscopi mrfm approach adopt bayesian metropoliswithingibb sampl framework perform bayesian semiblind algorithm spars imag superior previous propos semiblind algorithm altern minim algorithm blind algorithm develop natur imag illustr myopic algorithm real mrfm tobacco virus data

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