An application of the least squares plane fitting interpolation process to image reconstruction and enhancement

This work applies a least squares plane fitting (LSP) method as an alternative way of interpolating irregularly spaced pixel intensity values that are suitable for image reconstruction of a static scene via super-resolution (SR). SR is a term used within the computer vision and image processing community to describe the process of reconstructing a high resolution image from a sequence of several shifted images covering the same scene.   The accuracy attainable by this process is estimated via tests where the simulation parameters are controlled and where the reconstructed high resolution image can be compared with its original. In these tests the original image is scanned randomly so as to create a sequence of low-resolution and JPEG compressed shifted images. The comparison is based on the r.m.s.e. of the differences between the reconstructed image and the original

Super-resolution x-ray phase-contrast and dark-field imaging with a single 2D grating and electromagnetic source stepping

Here we report a method for increased resolution of single exposure three modality x-ray images using super-resolution. The three x-ray image modalities are absorption-, differential phase-contrast-, and dark-field-images. To create super-resolution, a non-mechanically movable micro-focus x-ray source is used. A series of almost identical x-ray projection images is obtained while the point source is translated in a two-dimensional grid pattern. The three image modalities are extracted from fourier space using spatial harmonic analysis, also known as the single-shot method. Using super-resolution on the low-resolution series of the three modalities separately results in high-resolution images for the modalities. This approach allows to compensate for the inherent loss in resolution caused by the single-shot method without increasing the need for stability or algorithms accounting for possible motion