ΑΙhub.org
 

Using neural networks to “upscale” old films

by
06 February 2020



share this:

The famous short, silent film L’arrivée d’un train en gare de La Ciotat, produced by Auguste and Louis Lumière in 1896, hit the news this week. AI developer Denis Shiryaev used a combination of Gigapixel AI and depth-aware video frame interpolation (DAIN) to “upscale” the film to 4k, 60 frames-per-second quality.

You can watch the upscaled video here:

Here is the original version for comparison:

There were two parts to creating this upscaled video. Firstly, the enhancement to 4k resolution. The algorithm used for this is based on neural networks and was trained with millions of photos. The training process helped to create a sophisticated network that learned the best way to enlarge, enhance, and create natural details.

In addition to the enhanced resolution, Shiryaev utilised DAIN to add frames per second. This video frame interpolation method was developed by Wenbo Bao and colleagues (Shanghai Jiao Tong University, University of California, Merced, and Google) and aims to synthesize new frames in between the original frames. In their arXiv article from April 2019 they propose a novel depth-aware video frame interpolation algorithm which explicitly detects occlusion (when one object in a 3D space is blocking another object from view) using depth information. They developed a depth-aware flow projection layer to synthesize intermediate flows that preferentially sample closer objects rather than those further away. Their algorithm also learns hierarchical features to gather contextual information from neighbouring pixels. The model then warps the input frames, depth maps, and contextual features within an adaptive warping layer. Finally, a frame synthesis network generates the output frame using residual learning.

Shiryaev has also added a colour version which was made using DeOldify. DeOldify was created by Jason Antic and employs Generative Adversarial Networks (GANs) to colorize black and white images.




Lucy Smith , Managing Editor for AIhub.
Lucy Smith , Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Lizard in your luggage? We’re using artificial intelligence to detect wildlife trafficking

Our research shows the potential for new technology to detect illegal wildlife in luggage or mail.
03 October 2022, by

Keeping learning-based control safe by regulating distributional shift

We propose a new framework to reason about the safety of a learning-based controller with respect to its training distribution.
30 September 2022, by

Bipedal robot achieves Guinness World Record in 100 metres

Cassie the robot, developed at Oregon State University, records the fastest 100 metres by a bipedal robot.
29 September 2022, by

#IJCAI2022 distinguished paper – Plurality veto: A simple voting rule achieving optimal metric distortion

How can we create a voting system that best represents the preferences of the voters?
28 September 2022, by

AIhub monthly digest: September 2022 – environmental conservation, retrosynthesis, and RoboCup

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
27 September 2022, by





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association