IEEE Trans Image Process - Local edge-preserving multiscale decomposition for high dynamic range image tone mapping.

Tópicos

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{ analysi(2126) use(1163) compon(1037) }
{ network(2748) neural(1063) input(814) }
{ spatial(1525) area(1432) region(1030) }
{ control(1307) perform(991) simul(935) }
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{ imag(1947) propos(1133) code(1026) }
{ time(1939) patient(1703) rate(768) }
{ method(2212) result(1239) propos(1039) }
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{ inform(2794) health(2639) internet(1427) }
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{ howev(809) still(633) remain(590) }
{ model(3480) simul(1196) paramet(876) }
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{ group(2977) signific(1463) compar(1072) }
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{ health(3367) inform(1360) care(1135) }
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{ data(2317) use(1299) case(1017) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ first(2504) two(1366) second(1323) }
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{ use(976) code(926) identifi(902) }
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{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.

Resumo Limpo

novel filter propos edgepreserv decomposit imag differ previous filter local adapt properti filter imag contain local mean everywher preserv local salient edg comparison made filter result result three method detail analysi also made behavior filter multiscal decomposit filter propos manipul high dynam rang imag three detail layer one base layer multiscal decomposit filter address three assumpt base layer preserv local mean everywher everi scale salient edg relat larg gradient local window nonzero gradient inform belong detail layer effect function also propos compress detail layer reproduc imag give good visual experiment result real imag demonstr algorithm especi effect preserv enhanc local detail

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