IEEE Trans Image Process - Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ measur(2081) correl(1212) valu(896) }
{ group(2977) signific(1463) compar(1072) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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{ process(1125) use(805) approach(778) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ state(1844) use(1261) util(961) }
{ cost(1906) reduc(1198) effect(832) }
{ structur(1116) can(940) graph(676) }
{ imag(1057) registr(996) error(939) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
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{ case(1353) use(1143) diagnosi(1136) }
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{ research(1218) medic(880) student(794) }
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{ data(2317) use(1299) case(1017) }
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{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a powerful video filtering algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher dimensional transform-domain representation of the observations is leveraged to enforce sparsity, and thus regularize the data: 3-D spatiotemporal volumes are constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are then grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e., self-similarity) along the fourth dimension of the group. Collaborative filtering is then realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, the collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original positions in the video. The proposed filtering procedure addresses several video processing applications, such as denoising, deblocking, and enhancement of both grayscale and color data. Experimental results prove the effectiveness of our method in terms of both subjective and objective visual quality, and show that it outperforms the state of the art in video denoising.

Resumo Limpo

propos power video filter algorithm exploit tempor spatial redund character natur video sequenc algorithm implement paradigm nonloc group collabor filter higher dimension transformdomain represent observ leverag enforc sparsiti thus regular data d spatiotempor volum construct track block along trajectori defin motion vector mutual similar volum group togeth stack along addit fourth dimens thus produc d structur term group differ type data correl exist along differ dimens local correl along two dimens block tempor correl along motion trajectori nonloc spatial correl ie selfsimilar along fourth dimens group collabor filter realiz transform group decorrel d separ transform shrinkag invers transform way collabor filter provid estim volum stack group return adapt aggreg origin posit video propos filter procedur address sever video process applic denois deblock enhanc grayscal color data experiment result prove effect method term subject object visual qualiti show outperform state art video denois

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