DeOldify is a set of Python/Jupyter/Machine Learning tools to add color and generally try to rescue aging photographs. Fully Open Source and requiring fairly heavy-weight hardware I thing that it is one to keep an eye on.
Some examples from the project link:
Maria Anderson as the Fairy Fleur de farine and Lyubov Rabtsova as her page in the ballet “Sleeping Beauty” at the Imperial Theater, St. Petersburg, Russia, 1890.
the diagonal shadow of the telephone wire on granddad’s forehead is likely causing some discoloration there…
Some tidy-up in GIMP:
and a run thru the deOldifier:
How about people who aren’t Caucasian? Or pictures that aren’t old; e.g., PlayRaw images that are rendered B&W? As there are many ways to go from colour to B&W, it would be interesting to compare the various B&W interpretations of a single source image.
I believe it depends on the original machine-learning training set being diverse enough, ethnically speaking. Most of the papers mention this, and indeed deOldify’s paper has non-caucasian examples.
@Steve_Barnes Thanks for providing some examples. Who wouldn’t want to train with Jackie Chan? He visited my dad’s nursing home once and they got to shake hands. Now, the nursing home has a large portrait of the man at one of its locations.
That website is hosting the so-called Iizuka, et al. 2016 colorize algorithm. iDeepcolor came a year after in the UC Berkeley paper R. Zhang, et al. 2017. deOldify is newer/different in that it is based on a SAGAN (H. Zhang 2018), instead of a GAN like in previous years’ technology.
Thanks @afre - great story. What I like about this ML example is that it has been made so accessible - anybody with a Google Drive account (free) can go to Google Colaboratory and step through the examples without installing a single item. Then, just before executing the last cell upload their own images to the specified directory on their google drive and run it on them. The Google colab service provides all of the computing power. Note that the Co-Lab notebook was contributed by Matt Robinson - great job.
@HIRAM - not only was the original training of a reasonably ethnically diverse set of images of people it also included scenes with no people in - I think that the author, Jason Antic, has done a very good job on this. Also much kudos to the folks at ImageNet for putting together the training data that was used.
Photo taken by my grandfather in Guam during WWII.
deOldify does a solid job on CoLab, which gives you a 11GB GPU. I usually find running the default render_factor=42 just about tops it off when processing your images. The default output was a start for some color rotation/de-flourescification in RT5.5, brief touchup in GIMP2.10: Notice the ox is slobbering on the sugar cane.
He visited my dad’s nursing home once and they got to shake hands. Now, the nursing home has a large portrait of the man at one of its locations.
Notice the ox is slobbering on the sugar cane. 
