Adversarial Training of SwinIR Model for Face Super-Resolution Processing

Abstract

This research aims to apply the SwinIR model to perform face image super-resolution processing using adversarial training techniques, thereby enhancing facial image features. With the rapid development of computer vision and artificial intelligence technologies, face image super-resolution processing plays a crucial role in improving the accuracy and performance of facial recognition and related applications. Firstly, we introduce the basic principles of adversarial training and provide a detailed overview of the architecture and characteristics of the SwinIR model. This model demonstrates excellent performance in super-resolution tasks, exhibiting high feature extraction and image reconstruction capabilities. Next, we describe the experimental design and dataset selection. Through extensive experiments, we compare the quality and feature representation of face images before and after super-resolution. The results show that after undergoing super-resolution processing with the SwinIR model, facial images exhibit significantly enhanced details and edges, leading to a notable improvement in image features. In summary, this research successfully enhances the feature information of facial images by applying the SwinIR model through adversarial training, effectively improving the details of the face. This research outcome is of significant importance in advancing the field of computer vision and enhancing the efficiency and accuracy of artificial intelligence technologies in practical applications.