IEEE Trans Image Process - Video deblurring algorithm using accurate blur kernel estimation and residual deconvolution based on a blurred-unblurred frame pair.

Tópicos

{ motion(1329) object(1292) video(1091) }
{ imag(2830) propos(1344) filter(1198) }
{ algorithm(1844) comput(1787) effici(935) }
{ can(774) often(719) complex(702) }
{ imag(1057) registr(996) error(939) }
{ model(2341) predict(2261) use(1141) }
{ inform(2794) health(2639) internet(1427) }
{ learn(2355) train(1041) set(1003) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ medic(1828) order(1363) alert(1069) }
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{ first(2504) two(1366) second(1323) }
{ imag(1947) propos(1133) code(1026) }
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{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ perform(999) metric(946) measur(919) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Blurred frames may happen sparsely in a video sequence acquired by consumer devices such as digital camcorders and digital cameras. In order to avoid visually annoying artifacts due to those blurred frames, this paper presents a novel motion deblurring algorithm in which a blurred frame can be reconstructed utilizing the high-resolution information of adjacent unblurred frames. First, a motion-compensated predictor for the blurred frame is derived from its neighboring unblurred frame via specific motion estimation. Then, an accurate blur kernel, which is difficult to directly obtain from the blurred frame itself, is computed using both the predictor and the blurred frame. Next, a residual deconvolution is applied to both of those frames in order to reduce the ringing artifacts inherently caused by conventional deconvolution. The blur kernel estimation and deconvolution processes are iteratively performed for the deblurred frame. Simulation results show that the proposed algorithm provides superior deblurring results over conventional deblurring algorithms while preserving details and reducing ringing artifacts.

Resumo Limpo

blur frame may happen spars video sequenc acquir consum devic digit camcord digit camera order avoid visual annoy artifact due blur frame paper present novel motion deblur algorithm blur frame can reconstruct util highresolut inform adjac unblur frame first motioncompens predictor blur frame deriv neighbor unblur frame via specif motion estim accur blur kernel difficult direct obtain blur frame comput use predictor blur frame next residu deconvolut appli frame order reduc ring artifact inher caus convent deconvolut blur kernel estim deconvolut process iter perform deblur frame simul result show propos algorithm provid superior deblur result convent deblur algorithm preserv detail reduc ring artifact

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