Deinterlacing algorithm using direction-oriented inverse-free Wiener filtering

Abstract

In this paper, we introduce a direction-oriented covariance-based adaptive deinterlacing (CAD) method. The conventional CAD method preserves edge direction; however, it does not consider the local edge direction, which makes it unsuitable for real-time applications. The proposed method only utilizes the neighbor pixels aligned in the local edge direction to obtain optimal coefficients, with less processing time than the aforementioned CAD. First, the local edge direction is determined using the modified edge-based line average method. Then, based on direction-oriented geometric duality, the optimal interpolation coefficients for the neighbor pixels in the corresponding direction are estimated using the Wiener filter. Furthermore, we present inverse-free least squares approximations using orthogonal-triangular decomposition based on the Gram-Schmidt process. This technique's performance improves as the size of the image becomes larger. The experimental results prove that the proposed method provides a significant improvement over other deinterlacing methods.