Vision-based magnification of corneal endothelium frames

We present a fast and effective method to compute a high-resolution image of the corneal endothelium starting from a low-resolution video sequence obtained with a general purpose biomicroscope. Our goal is to exploit information redundancy in the sequence so as to achieve via software a magnification power and an image quality typical of dedicated hardware, such as the confocal microscope. The method couples SVM training with graph-based registration, and explicitly takes into account the characteristics of the application domain. Results on long, real sequences and comparative tests against general-purpose super-resolution approaches are presented and discussed.