Enhanced Image Restoration Techniques Using Generative Facial Prior Generative Adversarial Networks in Human Faces in Comparison of PSNR with GPEN

D. Shravan, G. Ramkumar

2023

Abstract

The primary objective of this study is to discover whether or not Novel Generative Facial Prior Generative Adversarial Networks (GFPGAN) are capable of properly recreating human portraits from damaged face photographs. The purpose of this research is to assess the effectiveness of various image restoration techniques by contrasting the findings generated by the Generative Adversarial Networks (GAN) Prior Embedded Network (GPEN) approach with the quality of the restored pictures, as measured by the Peak Signal to Noise Ratio (PSNR). For the purpose of this study, a total of 464 samples were collected, split into two groups with 232 samples each. In Group 1, the researchers utilize a novel GFPGAN approach, whereas in Group 2, researchers use the GPEN method. The pre-trained models are imported as part of the process of the study, and the Novel GFPGAN code has been included into Google Colab and run. The size of the sample is determined with the use of a statistical tool found online (clincalc.com) by combining the F-score data obtained from earlier research with the results of the current study. In the computation, the pretest power is set at 80%, which is a number that is held constant, and the value of alpha is 0.05. As a direct result of the simulation, the Novel GFPGAN approach achieved the greatest PSNR value, which was measured at 0.32, while the GPEN PSNR value was measured at 0.30. There is a statistically significant difference in the accuracy provided by the two algorithms, as shown by significance values of 0.003 (P0.05). In terms of the PSNR values, the Novel GFPGAN performs better than the GPEN approach for the dataset that was presented.

Paper Citation

in Harvard Style

Shravan D. and Ramkumar G. (2023). Enhanced Image Restoration Techniques Using Generative Facial Prior Generative Adversarial Networks in Human Faces in Comparison of PSNR with GPEN . In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 557-563. DOI: 10.5220/0012561700003739

in Bibtex Style

@conference{ai4iot23,
author={D. Shravan and G. Ramkumar},
title={Enhanced Image Restoration Techniques Using Generative Facial Prior Generative Adversarial Networks in Human Faces in Comparison of PSNR with GPEN },
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={557-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012561700003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Enhanced Image Restoration Techniques Using Generative Facial Prior Generative Adversarial Networks in Human Faces in Comparison of PSNR with GPEN
SN - 978-989-758-661-3
AU - Shravan D.
AU - Ramkumar G.
PY - 2023
SP - 557
EP - 563
DO - 10.5220/0012561700003739
PB - SciTePress