A Survey on Super Resolution for video Enhancement Using GAN
This is an incremental survey paper summarizing existing methods for video enhancement, relevant for researchers in computer vision and applications like surveillance and medical imaging.
This survey paper compiles recent research on using Generative Adversarial Networks (GANs) for super-resolution in images and videos, highlighting techniques like recursive learning, novel loss functions, and attention models to enhance visual quality, with evaluation metrics including PSNR and SSIM.
This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks. The studies covered in these summaries provide fresh techniques to addressing the issues of improving image and video quality, such as recursive learning for video super-resolution, novel loss functions, frame-rate enhancement, and attention model integration. These approaches are frequently evaluated using criteria such as PSNR, SSIM, and perceptual indices. These advancements, which aim to increase the visual clarity and quality of low-resolution video, have tremendous potential in a variety of sectors ranging from surveillance technology to medical imaging. In addition, this collection delves into the wider field of Generative Adversarial Networks, exploring their principles, training approaches, and applications across a broad range of domains, while also emphasizing the challenges and opportunities for future research in this rapidly advancing and changing field of artificial intelligence.