A Methodological Approach for Combining Super-Resolution and Pattern-Recognition to Image Identification

Image acquisition systems integrated with laboratory automation produce multi-dimensional datasets. An effective computational approach for automatic analysis of image datasets is given by pattern recognition methods; in some cases, it can be advantageous to accomplish pattern recognition with image super-resolution procedures. In this paper, we define a method derived from pattern recognition techniques for the recognition of artefacts and noise on set of images combined with super-resolution algorithms. The advantage of our approach is automatic artefacts recognition, opening the possibility to build a general framework for artefact recognition independently by the specific application where it is used.