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Spatio-temporal de-interlacing based on maximum likelihood estimation

This paper proposes a novel de-interlacing algorithm that can make up motion compensation (MC) errors by using maximum likelihood (ML) estimator. Firstly, a proper registration is performed between current field and its adjacent fields, and the progressive frame corresponding to the current field is found via ML estimator based on the computed registration information. Here, in order to obtain a stable solution, well-known bilateral total variation (BTV)-based regularization is applied. Next, possible feathering artifacts are detected on a block basis effectively. So, edge-directional interpolation is applied to the pixels where feathering artifact may happen, instead of the above-mentioned temporal de-interlacing. Experimental results show that the PSNR of our proposed algorithm is on average 4dB higher than that of previous studies and provides the best visual quality.