Image Registration for Perspective Deformation Recovery

This paper describes a hierarchical image registration algorithm to infer the perspective transformation that best matches a pair of images. This work estimates the perspective parameters by approximating the transformation to be piecewise affine. We demonstrate the process by subdividing a reference image into tiles and applying affine registration to match them in the target image. The affine parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers image tiles with subpixel accuracy. The corresponding image tiles are used to estimate a global perspective transformation. We demonstrate this approach on pairs of digital images subjected to large perspective deformation.