Super-resolution enhancement algorithms are used to esti-1. INTRODUCTION mate a high-resolution video still (HRVS) from several lowresolution frames, provided that objects within the digital image
Super-resolution enhancement techniques may be used sequence move with subpixel increments. A Bayesian multito estimate a high-resolution still from several low-resoluframe enhancement algorithm is presented to compute an tion video frames, provided that objects within the image HRVS using the spatial information present within each frame sequence move with subpixel increments [1]. To properly as well as the temporal information present due to object motion incorporate temporal correlations into the multiframe obbetween frames. However, the required subpixel-resolution moservation model, high-quality subpixel motion vectors must tion vectors must be estimated from low-resolution and noisy be estimated between video frames [2-4]. Methods for video frames, resulting in an inaccurate motion field which can adversely impact the quality of the enhanced image. Several attaining subpixel accuracy generally employ an interpolasubpixel motion estimation techniques are incorporated into tion of the image sequence frames, followed by the applicathe Bayesian multiframe enhancement algorithm to determine tion of a parametric or nonparametric motion estimation their efficacy in the presence of global data transformations scheme. The accuracy of the estimated motion fields has between frames (i.e., camera pan, rotation, tilt, and zoom) and a direct influence on the quality of the high-resolution independent object motion. Visual and quantitative comparivideo still (HRVS) image. sons of the resulting high-resolution video stills computed from The concept of multiframe enhancement was originally two video frames and the corresponding estimated motion fields introduced by Tsai and Huang [5], in which an observation show that the eight-parameter projective motion model is apmodel was defined for a sequence consisting of subpixel propriate for global scene changes, while block matching and shifts of the same scene. Stark and Oskoui formulated a Horn-Schunck optical flow estimation each have their own projection onto convex sets (POCS) algorithm to compute advantages and disadvantages when used to estimate independent object motion.