Earlier this year, Amir Avni used neural networks to troll the subreddit/r/Colorization -- a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot. What could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. First off, let's look at some of the results/failures from my experiments (scroll to the bottom for the final result). Today, colorization is usually done by hand in Photoshop. In short, a picture can take up to one month to colorize. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners. Yet, if you're new to deep learning terminology, you can read my previous two posts here and here, and watch Andrej Karpathy's lecture for more background.

Image colorization is an engaging topic in the field of image-to-image translation. Even though color photography was invented in 1907, It didn't become popular for the average person until the 1960s because of its expensiveness and inaccessibility. All the photography and videography up until then was done on Black & White. Colorizing these images was impossible--until the DeOldify deep learning model came to life. DeOldify is an open-source deep learning model created by Jason Antic, used to add high-quality colorization to grayscale images with amazing results.

Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. First off, let's look at some of the results/failures from my experiments (scroll to the bottom for the final result). Today, colorization is done by hand in Photoshop. To appreciate all the hard work behind this process, take a peek at this gorgeous colorization memory lane video. In short, a picture can take up to one month to colorize. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners.

In this tutorial, you will learn how to colorize black and white images using OpenCV, Deep Learning, and Python. Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input (for example, an ocean on a clear sunny day must be plausibly "blue" -- it can't be colored "hot pink" by the model). The novel approach we are going to use here today instead relies on deep learning. We will utilize a Convolutional Neural Network capable of colorizing black and white images with results that can even "fool" humans! To learn how to perform black and white image coloration with OpenCV, just keep reading!

In India, we celebrated the festival of color "Holi" last week. We celebrate the end of the winter with a splash of color because that's what the spring will bring us in a few days. When I was young, the celebrations were sparse. It was the decade of frugal parenting. We waited for festivals so eagerly because it meant parent approved outing and fun.