SuperResolution-aided recognition of cytoskeletons in scanning probe microscopy images

In this paper, we discuss the possibility to adopt SuperResolution (SR) methods as an important preparatory step to Pattern Recognition, so as to improve the accuracy of image content recognition and identification. Actually, SR mainly deals with the task of deriving a high-resolution image from one or multiple low resolution images of the same scene. The high-resolved image corresponds to a more precise image whose content is enriched with information hidden among the pixels of the original low resolution image(s), and corresponds to a more faithfully representation of the imaged scene. Such enriched content obviously represents a better sample of the scene which can be profitably used by Pattern Recognition algorithms. A real application scenario is discussed dealing with the recognition of cell skeletons in Scanning ProbeMicroscopy (SPM) single image SR. Results show that the SR allows us to detect and recognize important information barely visible in the original low-resolution image.