IEEE Trans Image Process - Variational bayesian super resolution.

Tópicos

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Resumo

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.

Resumo Limpo

paper address super resolut sr problem set degrad low resolut lr imag obtain high resolut hr imag accur estim subpixel motion lr imag signific affect perform reconstruct hr imag paper propos novel super resolut method hr imag motion paramet estim simultan util bayesian formul model unknown hr imag acquisit process motion paramet unknown model paramet stochast sens employ variat bayesian analysi develop two novel algorithm joint estim distribut unknown propos framework follow advantag incorpor uncertainti estim algorithm prevent propag error estim various unknown algorithm robust error estim motion paramet use fulli bayesian formul develop algorithm simultan estim algorithm paramet along hr imag motion paramet therefor fullyautom requir paramet tune also show propos motion estim method stochast general classic lucaskanad registr algorithm experiment result demonstr propos approach effect compar favor stateoftheart sr algorithm

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