  In this research, we explore different ways to improve generative adversarial
networks for video super-resolution tasks from a base single image
super-resolution GAN model. Our primary objective is to identify potential
techniques that enhance these models and to analyze which of these techniques
yield the most significant improvements. We evaluate our results using Peak
Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Our
findings indicate that the most effective techniques include temporal
smoothing, long short-term memory (LSTM) layers, and a temporal loss function.
The integration of these methods results in an 11.97% improvement in PSNR and
an 8% improvement in SSIM compared to the baseline video super-resolution
generative adversarial network (GAN) model. This substantial improvement
suggests potential further applications to enhance current state-of-the-art
models.
