Super-resolution-based magnification of endothelium cells from biomicroscope videos of the cornea

We present a practical, robust, and effective pipeline to compute a high-resolution (HR) image of the corneal endothelium starting from a low-resolution (LR) video sequence obtained with a general purpose slit lamp biomicroscope. An image quality typical of dedicated and more expensive confocal microscopes is achieved via software magnification by exploiting information redundancy in the video sequence. In particular, the HR image is generated from the best LR frames, obtained by identifying the most suitable endothelium video subsequence using a support vector machine-based learning approach, followed by a robust graph-based frame registration. Results on long, real sequences show that the proposed approach is fast and produces better quality images than both classical multiframe super-resolution approaches and commercial state-of-the-art mosaicing software. Only low-cost equipment is required that makes the proposed method a valid diagnostic tool and an affordable resource for medical practice in both developed and developing countries.