Generative Facial Prior Generative Adversarial Networks based Restoration of Degraded Facial images in Comparison of PSNR with Photo Upsampling via Latent Space Exploration

Applying a Novel Generative face Prior Generation Adversarial Network, this research aims to restore damaged face photographs. U sing Peak Signal Noise Ratio (PSNR) as a metric, we will compare the recovered picture quality using the Photo Upsampling via Latent Space Exploration (PULSE) approach to the original. Two groups, every containing 232 samples, were used to generate 464 samples for this research. One group used a novel GFPGAN, while the other utilizes a PULSE technique. The research procedure involves importing pre-trained models as well as implementing and executing the Novel GFPGAN code in Google Colab. Using the F -score from prior research and an online statistical tool (clincalc.com), the sample size is determined. The computation uses a constant value of 80% for pretest power and a value of 0.05 for alpha. According to the findings, the Novel GFPGAN achieved a greatest PSNR value of 0.32, while the PULSE PSNR value was 0.25, representing a significance level of 0.001 (P<0.05). According to the PSNR values, Novel GFPGAN outperforms PULSE technique considerably for the provided dataset.