Fusion-based edge-sensitive interpolation method for deinterlacing

This paper proposes a fusion-based edge-sensitive interpolation method (FEID) for intra-field deinterlacing. The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) data fusion by the linear minimum mean square-error estimation (LMMSE) technique. Specifically, three directional neighboring pixel sets are defined in three directions (45°, 90°, and 135°) for every missing pixel. And each set produces an estimate of the pixel to be interpolated with a Wiener filter. With the information that gathered from the three directional neighboring pixel sets, a more robust estimate is obtained by fusing these directional estimates with the LMMSE technique. For fast implementation, we propose a gradient-based region selection approach that classifies a local region into two different classes, Region 1 and Region 2. The LMMSE-based data fusion method is used in Region 1; a fast deinterlacing algorithm is used in Region 2 to reduce the computational complexity. Compared with existing deinterlacing methods, the proposed method FEID improves the visual quality of the interpolated edges while maintaining a higher peak signal-to-noise–ratio (PSNR) level.