Super-Resolved Monkeypox Image Dataset for Enhanced Classification and Detection

Monkeypox is a reemerging viral disease caused by the Monkeypox virus, posing a significant global health threat due to its rapid transmission and lack of widespread diagnostic tools. In the image domain, challenges arise from limited datasets and poor resolution of available images, hampering the development of automated classification and detection systems. To address this, we leveraged a GAN-based approach, specifically Real-ESRGAN, to improve the resolution and quality of a novel dataset comprising 3,165 images. Our model achieved an average SSIM of 0.850 and a PSNR of 33.83 dB, demonstrating its effectiveness in generating high-quality images suitable for training robust detection systems.