Karthik Sivarama Krishnan* and Koushik Sivarama Krishnan

Gen Nine Inc, United States

*Corresponding Author: Karthik Sivarama Krishnan, Gen Nine Inc, United States.

Received: April 30, 2022; Published: May 18, 2022

Abstract

High-resolution images are really helpful in various applications like medical diagnosis and hence the need for super-resolution has also increased significantly. Increasing the image resolution on various medical images like a chest X-ray or cell images can improve the accuracy of diagnosis by revealing previously unseen details. Using Image super-resolution also reduces the number of X-ray radiations required to render ultra high-quality imaging. Hence we applied super-resolution on X-rays using fine-tuned Swift-SRGAN architecture, which significantly improved the details on the chest X-rays. This helps in rendering super-resolution images from low-resolution images with less computational requirements. The proposed approach achieves a Structural Similarity Index Measure(SSIM) of 0.893 and a Peak Signal-to-Noise Ratio (PSNR) of 32.10.

Keywords: X-rays; Super-resolution; Swift-SRGAN; Generative Adversarial Network (GAN); Chest X-ray; Medical Imaging; Mobile Computing; Peak Signal-to-Noise Ratio (PSNR); Structural Similarity Index Measure (SSIM)