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Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Furthermore, a better resolution may substantially improve automatic detection and image segmentation results. The arrival of digital medical imaging technologies such as Computerized Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI) etc. has revolutionized modern medicine. Despite the advances in acquisition technology and the performance of optimized reconstruction algorithms over the two last decades, it is not easy to obtain an image at a desired resolution due to imaging environments, the limitations of physical imaging systems as well as quality-limiting factors such as Noise and Blur. A solution to this problem is the use of Super Resolution (SR) techniques which can be used for processing of such images. Various methods have been described over the years to generate and form algorithms which can be used for building on this concept of Super resolution. This paper details few of the types of medical imaginary, various techniques used to perform super resolution and the current trends which are being followed for the implementation of this concept.
Isaac et al. (Sun,) studied this question.