IEEE Trans Image Process - Nonlocal transform-domain filter for volumetric data denoising and reconstruction.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ imag(1947) propos(1133) code(1026) }
{ group(2977) signific(1463) compar(1072) }
{ data(3963) clinic(1234) research(1004) }
{ signal(2180) analysi(812) frequenc(800) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ control(1307) perform(991) simul(935) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
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{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ method(984) reconstruct(947) comput(926) }
{ import(1318) role(1303) understand(862) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ data(3008) multipl(1320) sourc(1022) }
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{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
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{ system(1050) medic(1026) inform(1018) }
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{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ 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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{ patient(1821) servic(1111) care(1106) }
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{ use(976) code(926) identifi(902) }
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{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

We present an extension of the BM3D filter to volumetric data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d-dimensional patches are stacked together in a (d+1)-dimensional array and jointly filtered in transform domain. While in BM3D the basic data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D "group." The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations. We evaluate the algorithm on denoising of volumetric data corrupted by Gaussian and Rician noise, as well as on reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.

Resumo Limpo

present extens bmd filter volumetr data propos algorithm bmd implement group collabor filter paradigm mutual similar ddimension patch stack togeth ddimension array joint filter transform domain bmd basic data patch block pixel bmd util cube voxel stack d group d transform appli group simultan exploit local correl present among voxel cube nonloc correl correspond voxel differ cube thus spectrum group high spars lead effect separ signal nois coeffici shrinkag invers transform obtain estim group cube adapt aggreg origin locat evalu algorithm denois volumetr data corrupt gaussian rician nois well reconstruct volumetr phantom data nonzero phase noisi incomplet fourierdomain kspace measur experiment result demonstr stateoftheart denois perform bmd effect exploit regular volumetr data reconstruct

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