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 :



The Machine Ethics Podcast: AI readiness with Tim El-Sheikh

In this episode, Ben chats with Tim El-Sheikh about ethical AI as the smarter AI, the importance of a business AI strategy, getting data ready, and more.
22 October 2021, by

Join our team of AIhub ambassadors!

We are looking for people to join us as AIhub ambassadors.
21 October 2021, by

Interview with Lily Xu – applying machine learning to the prevention of illegal wildlife poaching

Lily Xu tells us about her work applying machine learning and game theory to wildlife conservation.
20 October 2021, by

What bird is singing? Merlin Bird ID app offers instant answers

The Cornell Lab of Ornithology’s free Merlin Bird ID app can identify bird sounds.
19 October 2021, by

Distilling neural networks into wavelet models using interpretations

We propose a method which distills information from a trained DNN into a wavelet transform.
18 October 2021, by

Cynthia Rudin wins AAAI Squirrel AI Award

Duke professor becomes second recipient of AAAI Squirrel AI Award for pioneering socially responsible AI.
15 October 2021, by





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association