When Cats meet GANs

This assignment includes two parts: in the first part, we will implement a specific type of GAN designed to process images, called a Deep Convolutional GAN (DCGAN). We will train the DCGAN to generate grumpy cats from samples of random noise. In the second part, we will implement a more complex GAN architecture called CycleGAN for the task of image-to-image translation. We will train the CycleGAN to convert between different types of two kinds of cats (Grumpy and Russian Blue).

Gradient Domain Fusion

This project explores gradient-domain processing, a simple technique with a broad set of applications including blending, tone-mapping, and non-photorealistic rendering. The primary goal of this assignment is to seamlessly blend an object or texture from a source image into a target image.

Colorizing the Prokudin-Gorskii Photo Collection

The goal of this assignment is to take the digitized Prokudin-Gorskii glass plate images and, using image processing techniques, automatically produce a color image with as few visual artifacts as possible. In order to do this, you will need to extract the three color channel images, place them on top of each other, and align them so that they form a single RGB color image.