Improving Super-Resolution in Face Images by Modeling Image Degradation Using Pairs of High-Quality and Low-Quality Images

Improving image quality for identification and authentication in security and surveillance systems is of particular importance, and today, using artificial intelligence, the quality of images can be significantly improved. In this regard, the present paper, focusing on the details of face images, has improved the image failure detection model in the adversarial generator network, which led to a suitable performance in the meta-dissolving of face images. Most of the CNN networks that have been presented in recent years require a large set of images with appropriate annotations for proper performance, and they usually perform poorly in the case of degradation that have not been trained, which is addressed in this research to improve this challenge. In this work, pairs of high-quality and low-quality images are used to train the image degradation detection model; This information is then transferred to real images. The naturalness of the output images is one of the most important challenges in this field. The obtained results show that the criterion of perceptual similarity of the obtained image is equal to 38.4%, which is comparable to recent researches. As a result, using the proposed model, more natural images were produced