Enhance/upsample images with Generative Adversarial Networks using Python and Tensorflow 2.0
What you'll learn
- Create a generator architecture that upsamples an image by 4 times in each dimension
- Create a discriminator architecture that scores both realism and fidelity to the original image
- Modify custom written Keras layers to accept input images of any size without rebuilding the model
- Train the models on a Cloud TPU through Google CoLab
- Use the trained generator in a practical application to upsample your own images
Requirements
- My "High Resolution Generative Adversarial Networks (GANs)" course
- Python experience
- Convolutional neural network experience
- Basic familiarity with TensorFlow 2.0 and Keras
Description
We've all seen the gimmick in crime TV shows where the investigators manage to take a tiny patch of an image and magnify it with unrealistic clarity. Well today, Generative Adversarial Networks are making the impossible possible.Dive into this course where I’ll show you how easily we can take the fundamentals from my High Resolution Generative Adversarial Networks course and build on this to accomplish this impressive feat known as Super-resolution. Not only will you be able to train a Generator to magnify an image to 4 times it’s original size (that’s 16 times the number of pixel!), but it will take relatively little effort on our end.
Just as in the first course, we’ll use Python and TensorFlow 2.0 along with Keras to build and train our convolutional neural networks. And since training our networks will require a ton of computational power, we’ll once again use Google CoLab to connect to a free Cloud TPU. This will allow us to complete the training in just a few days without spending anything on hardware!
If this sounds enticing, take a few minutes to watch the free preview of the “Results!” lesson. I have no doubt that you will come away impressed.
Who this course is for:
- Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity
