Quantification of image registration error

Abstract

Image registration is a digital image processing technique that takes two or more of images of a scene in different coordinate systems and transforms them into a single coordinate system. Image registration is a necessary step in many advanced image processing techniques, such as multi-frame super-resolution. For that reason, registration accuracy is very crucial. While image registration is usually performed on images, one can perform the registration using metric images as well. This paper will present registration methods and their accuracies for various noise levels for the case of pure translational image motion. Registration techniques will be applied to the images themselves as well as to phase congruency images, gradient images, and edge-detected images. This study will also investigate registration of under-sampled images. Noise-free images are degraded using three types of noise: additive Gaussian noise, fixed-pattern noise along the column direction, and a combination of these two. The registration error is quantified for two registration algorithms with three different images as a function of the signal-to-noise ratio. A test on the usefulness of the image registration and registration accuracy performed on the intensity images of the Stokes imaging polarimeter. The Stokes images calculated before and after registration of the intensity images are compared to each other to show the improvement.