IEEE Trans Image Process - Variational Bayesian method for Retinex.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ estim(2440) model(1874) function(577) }
{ motion(1329) object(1292) video(1091) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ method(984) reconstruct(947) comput(926) }
{ result(1111) use(1088) new(759) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ system(1050) medic(1026) inform(1018) }
{ time(1939) patient(1703) rate(768) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
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Resumo

In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters. By assuming that the reflection function is piecewise continuous and illumination function is spatially smooth, we define the energy functions in the Gibbs distributions as a total variation function and a smooth function for the reflectance and the illumination, respectively. We then apply the variational Bayes approximation to obtain the approximation of the posterior distribution of unknowns so that the unknown images and hyperparameters are estimated simultaneously. Experimental results demonstrate the efficiency of the proposed method for providing competitive performance without additional information about the unknown parameters, and when prior information is added the proposed method outperforms the non-Bayesian-based Retinex methods we compared.

Resumo Limpo

paper propos variat bayesian method retinex simul interpret human visual system perceiv color construct hierarch bayesian model use gibb distribut prior distribut reflect illumin gamma distribut model paramet assum reflect function piecewis continu illumin function spatial smooth defin energi function gibb distribut total variat function smooth function reflect illumin respect appli variat bay approxim obtain approxim posterior distribut unknown unknown imag hyperparamet estim simultan experiment result demonstr effici propos method provid competit perform without addit inform unknown paramet prior inform ad propos method outperform nonbayesianbas retinex method compar

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