Subpixel Motion Estimation for Super-Resolution Image Sequence Enhancement

Abstract

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the digital image sequence move with subpixel increments. A Bayesian multi_frame enhancement algorithm is presented to compute an HRVS using the spatial information present within each frame as well as the temporal information present due to object motion between frames. However, the required subpixel-resolution motion vectors must be estimated from low-resolution and noisy video frames, resulting in an inaccurate motion field which can adversely impact the quality of the enhanced image. Several subpixel motion estimation techniques are incorporated into the Bayesian multiframe enhancement algorithm to determine their efficacy in the presence of global data transformations between frames (i.e., camera pan, rotation, tilt, and zoom) and independent object motion. Visual and quantitative comparisons of the resulting high-resolution video stills computed from two video frames and the corresponding estimated motion fields show that the eight-parameter projective motion model is appropriate for global scene changes, while block matching and Horn–Schunck optical flow estimation each have their own advantages and disadvantages when used to estimate independent object motion.