Fine-tuned Generative Adversarial Network-based Model for Medical Images Super-Resolution. (arXiv:2211.00577v3 [eess.IV] UPDATED)

In medical image analysis, low-resolution images negatively affect the
performance of medical image interpretation and may cause misdiagnosis. Single
image super-resolution (SISR) methods can improve the resolution and quality of
medical images. Currently, Generative Adversarial Networks (GAN) based
super-resolution models have shown very good performance. Real-Enhanced
Super-Resolution Generative Adversarial Network (Real-ESRGAN) is one of the
practical GAN-based models which is widely used in the field of general image
super-resolution. One of the challenges in medical image super-resolution is
that, unlike …

arxiv generative adversarial network images medical network