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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Understanding how image super-resolution works

The pursuit and desire to be able to make low-resolution images more detailed and of a higher resolution has been around for several decades. Super-resolution is a collection of techniques that are used to convert low-resolution images into very high-resolution images and is one of the most exciting fields of work for image processing engineers and researchers. Several approaches and methods have been built to achieve super-resolution of images, and they have all had varying levels of success toward their goal. However, in recent times, with the development of SRGANs, there has been a significant improvement regarding the amount of super-resolution that can be possible using any low-resolution image. 

But before we discuss SRGANs, let's learn about some concepts related to image super-resolution. ...

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