IEEE Trans Pattern Anal Mach Intell - On Bayesian Adaptive Video Super Resolution.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ studi(1119) effect(1106) posit(819) }
{ howev(809) still(633) remain(590) }
{ model(3404) distribut(989) bayesian(671) }
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{ network(2748) neural(1063) input(814) }
{ state(1844) use(1261) util(961) }
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{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
{ take(945) account(800) differ(722) }
{ learn(2355) train(1041) set(1003) }
{ imag(1947) propos(1133) code(1026) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ age(1611) year(1155) adult(843) }
{ sampl(1606) size(1419) use(1276) }
{ estim(2440) model(1874) function(577) }
{ method(984) reconstruct(947) comput(926) }
{ perform(999) metric(946) measur(919) }
{ perform(1367) use(1326) method(1137) }
{ model(3480) simul(1196) paramet(876) }
{ cost(1906) reduc(1198) effect(832) }
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{ ehr(2073) health(1662) electron(1139) }
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{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
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{ 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) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Although multi-frame super resolution has been extensively studied in past decades, super resolving real-world video sequences still remains challenging. In existing systems, either the motion models are oversimplified, or important factors such as blur kernel and noise level are assumed to be known. Such models cannot capture the intrinsic characteristics that may differ from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel and noise level while reconstructing the original high-res frames. As a result, our system not only produces very promising super resolution results outperforming the state of the art, but also adapts to a variety of noise levels and blur kernels. To further analyze the effect of noise and blur kernel, we perform a two-step analysis using the Cramer-Rao bounds. We study how blur kernel and noise influence motion estimation with aliasing signals, how noise affects super resolution with perfect motion, and finally how blur kernel and noise influence super resolution with unknown motion. Our analysis results confirm empirical observations, in particular that an intermediate size blur kernel achieves the optimal image reconstruction results.

Resumo Limpo

although multifram super resolut extens studi past decad super resolv realworld video sequenc still remain challeng exist system either motion model oversimplifi import factor blur kernel nois level assum known model captur intrins characterist may differ one sequenc anoth paper propos bayesian approach adapt video super resolut via simultan estim under motion blur kernel nois level reconstruct origin highr frame result system produc promis super resolut result outperform state art also adapt varieti nois level blur kernel analyz effect nois blur kernel perform twostep analysi use cramerrao bound studi blur kernel nois influenc motion estim alias signal nois affect super resolut perfect motion final blur kernel nois influenc super resolut unknown motion analysi result confirm empir observ particular intermedi size blur kernel achiev optim imag reconstruct result

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