Robust methods for high-quality stills from interlaced video in the presence of dominant motion

Abstract

We present robust algorithms which combine global motion compensation and motion adaption for deinterlacing in the presence of both dominant motion, such as camera zoom, pan, or jitter, and local motion, such as object motion. The dominant motion is modeled by a global affine warping and estimated by a gradient-based estimation method. Two alternative algorithms are proposed for compensation of the dominant motion: a bilinear interpolation based on the affine model, and a projections onto convex sets (POCS) based method that takes into account blurring in the image formation. It is important to note that the latter must be used if the blurring is severe enough to act as an anti-alias filter, which imposes an irreversible limit on the resolution improvement ability of any motion-compensated filter. Global motion-compensated images are then input to a motion-adaptive filter to detect and correct for those pixels where there exists local motion. A dynamic thresholding for motion detection is presented, with weighted directional-filtering for regions where motion is detected, to obtain the best results. Experimental results with application to obtaining high quality stills from video camcorders demonstrate the effectiveness of the proposed methods.