Super-Resolution of Medical Images Using Real ESRGAN
Priyanka Nandal, Sudesh Pahal, Ashish Khanna, Plácido Rogério Pinheiro
Abstract
Rich details in an image are constantly vital for medical image analysis to detect a broad extent of medical ailments. The diagnosis will be best served if the image is accessible in high resolution and the small details are preserved. Image super-resolution techniques based on deep learning can assist us in extracting spatial features from a low-resolution image captured with current technologies. The updated variant of the super-resolution technique known as Real Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), which produces 2D real-world images with excellent perceptual quality, is used in the present work. We investigate the suggested approach using four distinct medical image types: 1) brain MRI images from the BraTS dataset; 2) dermoscopy images from the ISIC skin cancer dataset; 3) cardiac ultrasound images from the CAMUS dataset; and 4) chest x-rays images from the MIMIC-CXR dataset. The employed architecture achieves improved visual results in comparison to the alternative innovative techniques for super-resolution. The observed findings are evaluated and contrasted both qualitatively and quantitatively with conventional approaches in terms of PSNR, SSIM, and MSE, and an improvement of up to 12% is obtained.