Advanced Adversarial Techniques for Enhanced Grayscale Video Colorization Using Deep Learning

This research uses deep learning to automatically colorize grayscale videos. Grayscale video frames are colored using the Generative Adversarial Network (GAN) architecture. A generator and a discriminator are both utilized in this architecture. Prior to being enhanced during adversarial training, the generator receives pre-training, utilizing a U-Net architecture. The research colorizes grayscale videos using a GAN, a deep learning method. A U-Net generator is initially pre-trained using L1 loss to enhance color predictions. The generator's predictions are then improved by using an adve rsarial training strategy and a discriminator that can tell the difference between real and fake color images. Realistic colorizations can be created using the generator more easily. GAN created colorized video frames with an average PSNR of 21.06748.and average SSIM value attained was 0.90886. These results show that this technique can be useful in various applications like virtual reality, medical imaging etc.