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Recently, deep learning-based convolutional neural networks method for image super-resolution has achieved remarkable performance in various fields including security surveillance, satellite imaging, and medical image enhancement. Although these approaches obtained improved performance in medical images, existing works only used a pre-processing step and hand-designed filter methods to improve the quality of medical images. Pre-processing step and hand-designed-based reconstructed medical image results are very blurry and introduce new noises in the images. Due to this, sometimes medical practitioners make wrong decisions, which are very dangerous for human beings. In this chapter, the authors explain that the hand-designed as well as deep learning-based approaches, including some image quality assessment metrics to open the gate to verify the images with different approaches, depend on the single image approach. Furthermore, they discuss some important types of medical images and their properties. |
