Abstract
This paper presents a maximum a posteriori (MAP) based intra-field
deinterlacing algorithm. In the proposed algorithm, we propose a hybrid
approach composed by point-wise and patch-wise measurements. The estimation
of the missing pixel is formulated as an MAP and minimizing the energy
function. By utilizing Bayes theory and some prior knowledge, the
missing pixel is estimated with a statistical-based approach and we
model the residual of the images as Gaussian and Laplacian distribution.
Under the MAP framework, the desired deinterlaced image corresponds
to the optimal reconstruction given the interlaced low resolution
image. Compared with existing deinterlacing algorithms, the proposed
algorithm improves peak signal-to-noise-ratio and the structural similarity
while maintaining high efficiency.
© 2014 IEEE
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