Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

Abstract

With the help of deep neural networks, video super-resolution (VSR) has made a huge breakthrough. However, these deep learning-based methods are rarely used in specific situations. In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, low-resolution (LR) datasets are downgraded from high-resolution (HR) datasets in a fixed manner. In this paper, we proposed a model based on Generative Adversarial Network (GAN) and edge enhancement to perform super-resolution (SR) reconstruction for LR and blur videos, such as closed-circuit television (CCTV). The adversarial loss allows discriminators to be trained to distinguish between SR frames and ground truth (GT) frames, which is helpful to produce realistic and highly detailed results. The edge enhancement function uses the Laplacian edge module to perform edge enhancement on the intermediate result, which helps further improve the final results. In addition, we add the perceptual loss to the loss function to obtain a higher visual experience. At the same time, we also tried training network on different datasets. A large number of experiments show that our method has advantages in the Vid4 dataset and other LR videos.

Video Super-Resolution Using Multi-Scale and Non-Local Feature Fusion

“…Song et al [50] used gradient mapping between high-resolution frames and lowresolution frames to regularize multi-frame fusion to achieve video super-resolution. Wang et al [51] used edge enhancement method on the basis of generative adversarial network, which further improves the visual experience of video super-resolution. Liu et al [52] proposed a real-time video super-resolution model based on neural structure search, and applied this method to mobile devices.…”

Electrification of Smart Cities

“…The final article to be presented is with regard to advanced closed-circuit televisions for smart city surveillance. Wang, Teng, and An [5] claimed that with the help of deep neural networks, video super-resolution has made a huge breakthrough. However, these deeplearning-based methods are rarely used in specific situations.…”

“…In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, lowresolution datasets are downgraded from high-resolution datasets in a fixed manner. Hence, Wang, Teng, and An [5] proposed a model based on Generative Adversarial Network and edge enhancement to perform super-resolution reconstruction for low-resolution and blurry videos, such as closed-circuit television footage. The adversarial loss allows discriminators to be trained to distinguish between super-resolution frames and ground truth frames, which is helpful to produce realistic and highly detailed results.…”