Author Topic: Using deep-learning to colourize B/W Apollo photos  (Read 2400 times)

Offline apollo16uvc

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Using deep-learning to colourize B/W Apollo photos
« on: September 05, 2019, 03:57:30 PM »
Those who have followed me know I build a new monster computer this month.

Today my new high-end video card arrived.
An RTX 2080 Super by MSI, model Gaming X Trio.

The RTX lineup contains special Tensor cores which accelerate deep-learning.

Deep-learning/machine-learning/convoluted neural networks have been used to automatically colourize B/W photos by using hundreds of thousands of colour photos as a reference. Its even possible to colourize B/W video this way.

One such program is DeOldify: https://github.com/jantic/DeOldify
And this website: https://colourise.sg/

I don't know if it will work with the amount of colour photos avaible, but I will be giving it a shot. Generating a dataset from the colour photos may take several days. DeOldify advices the even higher end RTX 2080 Ti with 11 gigabytes of video memory, I got the model just below it with 'just' 8 gigabytes.

Updates if anything interesting ever comes from this...
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Offline apollo16uvc

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Re: Using deep-learning to colourize B/W Apollo photos
« Reply #1 on: September 07, 2019, 06:27:38 PM »
After two days of hard work and troubleshooting, including making a dual boot for Ubuntu to run Anaconda with Jupyter Notebook the rendering system has finally been setup.

For now I have been trying out the different settings and colourizing normal photos with the pre-made models.

Hopefully I can look into training a model with Apollo photos in the following weeks.

Some of the more decent results so far from my collection:



It generally likes colouring buildings.




It also seems to enjoy the sky, I do like the results. Its smart enough to keep the clouds white!



It does alright at differentiating between articles of clothing, animals and humans. Like some other AI I have tried it is quick to make clothing/faces red or blue. It doesn't always work.

I have noticed that contrast, clarity and brightness of the image are a contributing factor in how it renders colours. I am sure the type of film (Orthocromatic, Pancromatic) is also a contributing factor.
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