Palette.fm is a free artificial intelligence (AI) tool that can turn black-and-white photos into color. Thanks to machine learning, it can identify what is in an image and take a good guess at applying the right color. If you've never colorized black-and-white photos before, the results will blow you away! Unlike other AI colorizer tools, the image can be fine-tuned using a text prompt or by selecting a different color palette. If you have some old black-and-white family photos, this is how you can bring them to life in a matter of minutes.

DeepAI is a research company that develops a wide variety of deep neural network (DNN) models using the bleeding edge of AI research. For example, they have built models for sentiment analysis of text, nudity detection, artistic style transfer, text summarization, etc. One model that I was particularly interested in using was the Image Colorization that adds realistic coloration to old black-and-white photos. In this post, I show how easy it is to use DeepAI's API for this model to color your own images automatically using Python. Using the API is very simple.

HIROSHIMA--Students here are recreating the lives of people devastated by the atomic bombing on Aug. 6, 1945, by vividly colorizing photos taken before the city was leveled by the nuclear attack. Students from Hiroshima Jogakuin Senior High School combine artificial intelligence technology and interviews with atomic bomb survivors to produce realistic coloring of black-and-white photos provided by hibakusha. Monochrome pictures snapped before and during World War II are automatically colorized with artificial intelligence. The processed photos are shown to hibakusha so colors can be manually adjusted based on their accounts. Converting one black-and-white photo into color takes from one week to several months, and 140 pictures have been colorized since November.

Researchers at Waseda University in Tokyo have created a way to realistically colorize black-and-white photos without any human intervention for the first time ever. The team's approach is based on convolutional neural networks -- a type of machine learning originally inspired by the visual cortex of a cat. The researchers used artificial intelligence to classify a full image and then identify parts of that image to label its components before filling them in with the appropriate colors. Previous research efforts in automated colorization fell short of being totally automatic. Most required users to provide a reference image that was similar to the black-and-white image in order to colorize it properly.

Want to inject some color to your photographs in a hurry? Well, new software can take an alarmingly good guess at what a color version of your black-and-white photographs may look like. Researchers from the University of California at Berkeley have developed a new computer vision system that takes a grayscale image and then adds color to it in a way that looks convincing to humans. That careful description is important: It doesn't necessarily choose the right colors, merely ones that look plausible. It uses what's known as a convolutional neural network to perform its party trick, having been trained on a set of over a million color images.