What are the most effective transfer learning techniques for image restoration and enhancement?
Image restoration and enhancement are important tasks in artificial intelligence (AI) that aim to improve the quality and clarity of images. They can be applied to various domains, such as medical imaging, remote sensing, security, and entertainment. However, image restoration and enhancement are challenging problems, as they often require large and diverse datasets, complex models, and high computational resources. Transfer learning is a technique that can help overcome these challenges by leveraging the knowledge learned from one domain or task to another. In this article, you will learn about some of the most effective transfer learning techniques for image restoration and enhancement, and how they can benefit your AI projects.