Enhanced Face Image Super-Resolution Using Generative Adversarial Network

Abstract

We proposed an Enhanced Face Image Generative Adversarial Network (EFGAN). Single image super-resolution (SISR) using a convolutional is often a problem in enhancing more refined texture upscaling factors. Our approach focused on mean square error (MSE), validation peak-signal-to-noise ratio (PSNR), and Structural Similarity Index (SSIM). However, the peak-signal-to-noise ratio has a high value to detail. The generative Adversarial Network (GAN) loss function optimizes the super-resolution (SR) model. Thus, the generator network is developed with skip connection architecture to improve performance feature distribution.