Medical image processing takes many steps to capture, process, and convert
the images for further analysis. The images are susceptible to distortions due to various
factors related to the analysis tools, environment, system-generated faults, and so on.
Image enhancement deals with enhancing the quality and resolution of images for
accurately analyzing the original information from the images. The primary motivating
aspect of research and reconstruction of such high-quality images and their challenges
is image super-resolution for image upgrading. This chapter focuses on various image-enhancing strategies in implementing the super-resolution process. In this work, the
methodologies of various image-enhancing strategies are explained clearly to provide
the parameter selection points, feature comparisons, and performance evaluations that
apply to high-resolution image processing. The drawbacks and challenges of each
strategy are discussed to investigate the effectiveness of the methodologies. Further
research is explored to find hybrid methods on various deep learning architectures to
achieve higher accuracy in the field of medical image super-resolution.
Keywords: Image enhancement, Image feature mapping, Image processing, Image registration, Image resolution, Image segmentation, Medical imaging.
