Implementation of Deep Learning Based Enhanced Resolution Paradigms for Medical Diagnostics: A Study

Medical imaging researchers frequently talk about the issue of super-resolution. Medical image resolution often remains unsatisfactory because of limitations in acquisition time, radiation dose, or hardware resources. The relevance of deep learning networks has accelerated computer vision development, dramatically improving medical imaging. Advanced deep learning algorithms have recently been habituated to address the finest resolution issues of images in medical diagnostics and have formed innovative results. This study explores the various super-resolution techniques proposed over the last five years.