IEEE Trans Image Process - Stable and Robust Sampling Strategies for Compressive Imaging.

Tópicos

{ signal(2180) analysi(812) frequenc(800) }
{ imag(1947) propos(1133) code(1026) }
{ spatial(1525) area(1432) region(1030) }
{ problem(2511) optim(1539) algorithm(950) }
{ measur(2081) correl(1212) valu(896) }
{ learn(2355) train(1041) set(1003) }
{ process(1125) use(805) approach(778) }
{ imag(2830) propos(1344) filter(1198) }
{ perform(1367) use(1326) method(1137) }
{ network(2748) neural(1063) input(814) }
{ chang(1828) time(1643) increas(1301) }
{ extract(1171) text(1153) clinic(932) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ framework(1458) process(801) describ(734) }
{ model(2220) cell(1177) simul(1124) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ intervent(3218) particip(2042) group(1664) }
{ use(1733) differ(960) four(931) }
{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ search(2224) databas(1162) retriev(909) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ result(1111) use(1088) new(759) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In many signal processing applications, one wishes to acquire images that are sparse in transform domains such as spatial finite differences or wavelets using frequency domain samples. For such applications, overwhelming empirical evidence suggests that superior image reconstruction can be obtained through variable density sampling strategies that concentrate on lower frequencies. The wavelet and Fourier transform domains are not incoherent because low-order wavelets and low-order frequencies are correlated, so compressive sensing theory does not immediately imply sampling strategies and reconstruction guarantees. In this paper, we turn to a more refined notion of coherence-the so-called local coherence-measuring for each sensing vector separately how correlated it is to the sparsity basis. For Fourier measurements and Haar wavelet sparsity, the local coherence can be controlled and bounded explicitly, so for matrices comprised of frequencies sampled from a suitable inverse square power-law density, we can prove the restricted isometry property with near-optimal embedding dimensions. Consequently, the variable-density sampling strategy we provide allows for image reconstructions that are stable to sparsity defects and robust to measurement noise. Our results cover both reconstruction by l1-minimization and total variation minimization. The local coherence framework developed in this paper should be of independent interest, as it implies that for optimal sparse recovery results, it suffices to have bounded average coherence from sensing basis to sparsity basis-as opposed to bounded maximal coherence-as long as the sampling strategy is adapted accordingly.

Resumo Limpo

mani signal process applic one wish acquir imag spars transform domain spatial finit differ wavelet use frequenc domain sampl applic overwhelm empir evid suggest superior imag reconstruct can obtain variabl densiti sampl strategi concentr lower frequenc wavelet fourier transform domain incoher loword wavelet loword frequenc correl compress sens theori immedi impli sampl strategi reconstruct guarante paper turn refin notion coherenceth socal local coherencemeasur sens vector separ correl sparsiti basi fourier measur haar wavelet sparsiti local coher can control bound explicit matric compris frequenc sampl suitabl invers squar powerlaw densiti can prove restrict isometri properti nearoptim embed dimens consequ variabledens sampl strategi provid allow imag reconstruct stabl sparsiti defect robust measur nois result cover reconstruct lminim total variat minim local coher framework develop paper independ interest impli optim spars recoveri result suffic bound averag coher sens basi sparsiti basisa oppos bound maxim coherencea long sampl strategi adapt accord

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