Frame Acceleration and Super Resolution for video Enhancement Using GAN

Low-resolution (LR) videos are common in various applications, including surveillance and video conferencing, primarily due to bandwidth constraints or older recording technologies. This research introduces a novel approach for enhancing the quality of LR videos by leveraging Generative Adversarial Networks (GANs) equipped with MeanShift loss, perceptual loss, Total Variation (TV) Loss, and a modified VGG19 architecture. By processing each frame of an LR video through our model, we achieve significant improvements in visual quality, detail sharpness, and temporal consistency, resulting in high-resolution (HR) videos that are both visually pleasing and realistic. The conventional approach to video enhancement, particularly when employing deep learning models like GANs, involves processing high volumes of data, which can be computationally intensive and time-consuming. This challenge is compounded in real-time applications or environments lacking powerful graphical processing units (GPUs). Our research proposes a novel solution by extracting frames from low-resolution (LR) videos, enhancing each frame individually, and then recombining them into a high-resolution (HR) video. This method offers a significant reduction in computational requirements, making high-quality video enhancement accessible on less capable hardware and suitable for real-time processing.