Maximum likelihood estimation-based de-interlacing

This paper proposes a novel de-interlacing algorithm that reconstructs missing pixels by using maximum likelihood (ML) estimator. Firstly, a proper registration is performed between a 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 artifacts are detected on a block basis effectively. If possible artifacts are detected, block-based directional interpolation is applied to those blocks. Experimental results show that the proposed algorithm outperforms previous studies in terms of visual quality as well as PSNR.