Comparison of super-resolution methods for quality enhancement of digital biomedical images

The problem of resolution enhancement has been recently attracted the image processing community both for its theoretical and applications relevance. Achieving an higher and higher resolution capability is the objective of imaging sensor technology, which is often paid in terms of high equipment costs. On the other hand, the advances in signal processing theory and equipment make solutions for resolution enhancement based on post-processing of low-resolutions acquisitions appealing. Some type of biomedical imaging systems, such as computer tomography or magnetic resonance, are specific examples that can benefit from super-resolution of images. In this paper, we review some advanced techniques available for single image super-resolution and propose a variation of one method based on sparse representations. Then, we compare the performance of each method when they are applied to the quality enhancement of low-resolution biomedical images.