Superresolution of a Region of Interest Using Feature-Based Affine Motion Estimation

This paper presents a low-computational superresolution method of a region of interest (ROI) using multiple low-resolution input images of the same scene. We extract the regions from the multiple input images that are similar to the selected ROI in the reference image and use feature points to estimate the affine motion parameters. We apply a projection onto convex sets based method to interpolate the ROI using the estimated motion and simplify the iterative computation of the whole system, in which an edge-preserving smoothing filter is utilized to reduce the motion compensation error caused by additive noise. Experiments with several test image sets show the effectiveness of the proposed method.