Multiframe integration via the projective transformation with automated block matching feature point selection

Abstract

A subpixel-resolution image registration algorithm based on the nonlinear projective transformation model is proposed to account for camera translation, rotation, zoom, pan, and tilt. Typically, parameter estimation techniques for transformation models require the user to manually select feature points between the images undergoing registration. In this research, block matching is used to automatically select correlated feature point pairs between two images, and these features are used to calculate an iterative least squares solution for the projective transformation parameters. Since block matching is capable of estimating accurate translation motion vectors only in discontinuous edge regions, inaccurate feature point pairs are statistically eliminated prior to computing the least squares parameter estimate. Convergence of the projective transformation model estimation algorithm is generally achieved in several iterations. After subpixel-resolution image registration, a high-resolution video still may be computed by integrating the registered pixels from a short sequence of low-resolution image sequence frames.