Deinterlacing algorithm using gradient-guided interpolation and weighted average of directional estimation

This study presents a novel intra-field deinterlacing algorithm using gradient-guided interpolation (GGI) and weighted average of directional estimation (WADE). In this method, the authors classify each missing pixel into two categories according to different local region gradient features. After the classification of the local region, one of the two different interpolation methods is chosen for the corresponding region. The two interpolation methods are GGI method and the interpolation method using WADE. The average weighted method is used for smooth or texture regions because of its high interpolation performance by adopting the directional detail information among the neighbouring pixels. On the other hand, the GGI method is used for complex regions with strong or weak edges since it has high edge-preserving ability because of considering not only the similarity of pixel intensities, but also the pixel gradient features. The WADE and GGI methods are the main contributions of this study. Compared with the traditional deinterlacing methods, the proposed method improves both the objective and subjective qualities of the interpolated images. In addition, the proposed method possesses a very low computational burden compared with most intra-field deinterlacing methods.