Detection of aliasing in image sequences using nonlinear factor analysis

Abstract

In computational imaging, reconstructing a single high-reso- lution scene from multiple low-resolution aliased images is most efficient if done only over those regions where significant aliasing occurs. This paper presents a framework for detecting pixel locations exhibiting the most-prominent effects of aliasing in a sequence of subpixel-shifted images. The process employs nonlinear factor analysis of the image sequence, in which the latent variables are the relative position offsets for each image in the sequence, followed by outlier detection on the error residuals from the joint estimation process. Numerical examples illustrate the capabilities of the methodology. © 2012 Springer-Verlag.