Super-resolution imaging: use of zoom as a cue

摘要

In this paper, we propose a novel technique for super-resolution imaging of a scene from observations at different zoom levels. Given a sequence of images with different zoom factors of a static scene, the problem is to obtain a picture of the entire scene at a resolution corresponding to the most zoomed image in the scene. We not only obtain the super-resolved image for known integer zoom factors, but also for unknown arbitrary zoom factors. We model the super-resolution image as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation method is used to derive a cost function which is then optimized to recover the high-resolution field. The entire observation conforms to the same MRF, but is viewed at the different resolution pyramid. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution techniques, we do away with the correspondence problem. Results of the experimentation on real data are presented.