IEEE Trans Image Process - LLSURE: local linear SURE-based edge-preserving image filtering.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ method(1557) propos(1049) approach(1037) }
{ algorithm(1844) comput(1787) effici(935) }
{ estim(2440) model(1874) function(577) }
{ framework(1458) process(801) describ(734) }
{ imag(1947) propos(1133) code(1026) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(2220) cell(1177) simul(1124) }
{ featur(1941) imag(1645) propos(1176) }
{ risk(3053) factor(974) diseas(938) }
{ model(3480) simul(1196) paramet(876) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ motion(1329) object(1292) video(1091) }
{ chang(1828) time(1643) increas(1301) }
{ import(1318) role(1303) understand(862) }
{ studi(1119) effect(1106) posit(819) }
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{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
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{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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{ network(2748) neural(1063) input(814) }
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{ perform(999) metric(946) measur(919) }
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{ record(1888) medic(1808) patient(1693) }
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{ 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) }
{ 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) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Resumo Limpo

paper propos novel approach perform highqual edgepreserv imag filter base local linear model use principl stein unbias risk estim estim mean squar error noisi imag deriv simpl explicit imag filter can filter nois preserv edg finescal detail moreov filter fast exact lineartim algorithm whose comput complex independ filter kernel size thus can appli real time imag process task experiment result demonstr effect new filter various comput vision applic includ nois reduct detail smooth enhanc high dynam rang compress flashnoflash denois

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