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. © 2013 Springer-Verlag.
CITATION STYLE
Comanducci, D., & Colombo, C. (2013). Vision-based magnification of corneal endothelium frames. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7963 LNCS, pp. 52–61). https://doi.org/10.1007/978-3-642-39402-7_6
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