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Hands-On Image Generation with TensorFlow

You're reading from  Hands-On Image Generation with TensorFlow

Product type Book
Published in Dec 2020
Publisher Packt
ISBN-13 9781838826789
Pages 306 pages
Edition 1st Edition
Languages
Author (1):
Soon Yau Cheong Soon Yau Cheong
Profile icon Soon Yau Cheong
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: Fundamentals of Image Generation with TensorFlow
2. Chapter 1: Getting Started with Image Generation Using TensorFlow 3. Chapter 2: Variational Autoencoder 4. Chapter 3: Generative Adversarial Network 5. Section 2: Applications of Deep Generative Models
6. Chapter 4: Image-to-Image Translation 7. Chapter 5: Style Transfer 8. Chapter 6: AI Painter 9. Section 3: Advanced Deep Generative Techniques
10. Chapter 7: High Fidelity Face Generation 11. Chapter 8: Self-Attention for Image Generation 12. Chapter 9: Video Synthesis 13. Chapter 10: Road Ahead 14. Other Books You May Enjoy

Image processing

Out of all the things that image generative models can do, image processing is probably the one that produces the best results for commercial use. In our context, image processing refers to applying some transformation to existing images to produce new ones. We will look at the three applications of image processing in this section – image inpainting, image compression, and image super-resolution (ISR).

Image inpainting

Image inpainting is the process of filling in missing pixels of an image so that the result is visually realistic. It has practical applications in image editing, such as restoring a damaged image or removing obstructing objects. In the following example, you can see how image inpainting is used to remove people in the background. We first fill the people in with white pixels, then we use a generative model to fill in the pixels:

Figure 10.2 – Image inpainting using DeepFillv2 to remove people in the background...

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