IEEE Trans Image Process - Image Sensor Noise Parameter Estimation by Variance Stabilization and Normality Assessment.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ estim(2440) model(1874) function(577) }
{ model(3480) simul(1196) paramet(876) }
{ take(945) account(800) differ(722) }
{ problem(2511) optim(1539) algorithm(950) }
{ imag(1947) propos(1133) code(1026) }
{ error(1145) method(1030) estim(1020) }
{ perform(1367) use(1326) method(1137) }
{ implement(1333) system(1263) develop(1122) }
{ network(2748) neural(1063) input(814) }
{ method(1557) propos(1049) approach(1037) }
{ model(2220) cell(1177) simul(1124) }
{ featur(1941) imag(1645) propos(1176) }
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{ state(1844) use(1261) util(961) }
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{ structur(1116) can(940) graph(676) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
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{ data(3963) clinic(1234) research(1004) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

High-quality image denoising requires taking into account the dependence of the noise distribution on the original image. The parameters of this dependence are often unknown and we propose a new method to estimate them here. Using an optimization procedure, we find a variance-stabilizing transformation, which transforms the input image into an image with signal-independent noise. Principal component analysis of blocks of the transformed image allows estimation of the variance of the signal-independent noise so that the parameters of the original noise model can be computed. The image blocks for processing are selected in such a way that they have low stochastic texture strength but preserve the noise distribution. The algorithm does not require the original image to have homogeneous areas and can accurately process images with regular textures. It has high computational efficiency and smaller maximum estimation error compared with the state of the art. Our experiments have also shown that denoising with the noise parameters estimated by this method leads to the same results as denoising with the true noise parameters.

Resumo Limpo

highqual imag denois requir take account depend nois distribut origin imag paramet depend often unknown propos new method estim use optim procedur find variancestabil transform transform input imag imag signalindepend nois princip compon analysi block transform imag allow estim varianc signalindepend nois paramet origin nois model can comput imag block process select way low stochast textur strength preserv nois distribut algorithm requir origin imag homogen area can accur process imag regular textur high comput effici smaller maximum estim error compar state art experi also shown denois nois paramet estim method lead result denois true nois paramet

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