  In the field of medical image analysis, there is a substantial need for
high-resolution (HR) images to improve diagnostic accuracy. However, it is a
challenging task to obtain HR medical images, as it requires advanced
instruments and significant time. Deep learning-based super-resolution methods
can help to improve the resolution and perceptual quality of low-resolution
(LR) medical images. Recently, Generative Adversarial Network (GAN) based
methods have shown remarkable performance among deep learning-based
super-resolution methods. Real-Enhanced Super-Resolution Generative Adversarial
Network (Real-ESRGAN) is a practical model for recovering HR images from
real-world LR images. In our proposed approach, we use transfer learning
technique and fine-tune the pre-trained Real-ESRGAN model using medical image
datasets. This technique helps in improving the performance of the model. We
employ the high-order degradation model of the Real-ESRGAN which better
simulates real-world image degradations. This adaptation allows for generating
more realistic degraded medical images, resulting in improved performance. The
focus of this paper is on enhancing the resolution and perceptual quality of
chest X-ray and retinal images. We use the Tuberculosis chest X-ray (Shenzhen)
dataset and the STARE dataset of retinal images for fine-tuning the model. The
proposed model achieves superior perceptual quality compared to the Real-ESRGAN
model, effectively preserving fine details and generating images with more
realistic textures.
