Abstract
Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, we provide an overview of image resolution and the deep learning introduced in super resolution. This document describes super resolution for single images versus super resolution for multiple images, evaluation metrics and loss functions.
Highlights
Despite the rapid development of imaging technology, imaging devices still have limited achievable resolution due to numerous theoretical and practical limitations
Loss functions are used as rebuild score metrics and for model optimization
The PSNR refers to the mean square error (MSE) or the loss function L2 and provides information on the differences at the pixel level
Summary
Despite the rapid development of imaging technology, imaging devices still have limited achievable resolution due to numerous theoretical and practical limitations. Various fundamental methods have been developed for SR, such as non-uniform interpolation, frequency domain, and machine learning-based reconstruction approaches. While these methods can provide optimal or near-optimal images that increase resolution, they cannot guarantee improvements in detail, such as: Loss of high-frequency information and edge blurring. To solve these problems, deep learning-based SR methods are being developed, as the mapping relationships of image characteristics from LR to HR can be fully examined and the reconstruction results show robustness and reliability, remarkable stability in multi-space -ladder. Deep learning-based SR methods are being developed, as the mapping relationships of image characteristics from LR to HR can be fully examined and the reconstruction results show robustness and reliability, remarkable stability in multi-space -ladder. [6][7]
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