Have you ever needed a high-resolution image?

 

Have you ever wondered how cult classic films like The Godfather, To Kill A Mockingbird, and Star Wars, exist today in resolutions like 4k? Despite being released in the last century, when technology was far from where it is today, how has humanity still preserved these pieces of art? 

 

You go to a web application to upload your image, upscale it to 8X, and the image is super blurry.

 

Why not build your own image upscaler? 

 

Join us on our Live Tutorial on 17th June at 9:30 AM EST to learn all about Super Resolution GANs.

 

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GANs, or Generative Adversarial Networks, have taken the world by storm. They can write plays like the bard and paint like Picasso and even generate high-resolution images of EEG signals from our brain!

 

We have tutorials on training a model for Super-Resolution and Enhanced Super Resolution GAN that will allow you to upscale your own images.

 

Complete code walkthrough live on our YouTube channel, so subscribe now if you haven't already.

 

The lesson will be streamed live on 📆  Friday 17th June at 9:30 am EST. Here is the link to the YouTube live stream 👇
 

How to enhance image resolution with GANs?

We recommend you click the set-reminder 🔔  button on the live stream to avoid missing out.

 

This event is for you if:

  • You want to learn Super-resolution GANs 
  • You want to understand the architecture of GANs
  • You want to learn interactively and ask questions
  • You want a complete code walkthrough
  • You can join us next Friday at 9:30 am EST

 

So mark your calendars and stay tuned for further updates.

 

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PyImageSearch Team

 

P.S: We will have a surprise planned for you at the end of the live stream. Do not miss it!


P.S. If you're interested in learning how to successfully apply deep learning to your own projects, I would recommend reading my book, Deep Learning for Computer Vision with Python.

Inside the book you'll find:
  • Super-practical walkthroughs that present solutions to actual real-world image classification (ResNet, VGG, etc.), object detection (Faster R-CNN, SSDs, RetinaNet, etc.), and segmentation (Mask R-CNN) problems
  • Hands on tutorials (with lots of code) that show you not only the algorithms behind deep learning for computer vision but their implementations as well.
  • A no-nonsense teaching style that is guaranteed to help you master deep learning for image understanding and visual recognition
If you're interested in learning more about the book, I'd be happy to send you a PDF containing the Table of Contents and a few sample chapters:

Click here to grab the PDF of sample chapters and Table of Contents

After clicking the above link, you'll receive a separate email with the PDF in a few short moments.