CNN vs GAN in image processing: A comparative analysis

This study compares convolutional neural networks (CNNs) with generative adversarial networks (GANs) in image processing. Image classification and recognition features have been revolutionized through CNNs because of their widespread use in image classification tasks. GANs demonstrate high potential for creating realistic synthesized images that prove useful for enhancing images and carrying out creative work. The evaluation focuses on the unique capabilities and difficulties of CNNs and GANs regarding their utilization across medical, security, and entertainment fields. Research shows that CNNs demonstrate superior performance in classification applications, yet GANs lead image generation operations, particularly through projects like image restoration and inpainting tasks. CNNs and GANs work together in image processing because they provide separate abilities to address various real-world image processing needs.