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Last pushed: a year ago
Short Description
Colorizing and up-scaling video (on your CPU)
Full Description


We used pretrained convolutional neural networks (CNNs) to automatically colorize, denoise, and upscale a portion of Godard's Breathless (1960).

We accomplished this by leveraging pretrained models released by the authors of two recent papers:
[1] Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. "Learning Representations for Automatic Colorization," In arXiv, Mar 2016.
[2] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, "Image Super-Resolution Using Deep Convolutional Networks", In arXiv, Jan 2015.

We created a docker container so users can easily colorize, denoise, and upscale their own videos on any machine that has docker installed (although you'll need a good GPU to do this quickly).

<a href=" " target="_blank"><img src="" alt="autocolorization" width="848" height="478" border="10" /></a>

Examples & Usage

Colorizing your video:

1) Break video into individual frames and extract audio

./ your_video.avi

2) Run colorization on frames


3) Recreate video using the colorized frames

./ new_frames

Colorizing and upscaling your video:

1) Break video into individual frames and extract audio

./movie2frames your_video.mp4 frames png

2) Run colorization on frames


3) Denoise and upscale the frames

find ./frames -name "*.png" |sort > frames.txt
mkdir new_frames
[lua call]

4) Recreate video with colorized and upscaled frames

avconv -f image2 -r 24 -i new_frames/%d.png -i audio.mp3 -r 24 -vcodec libx264 -crf 16 video.mp4

Installation & Setup

We provide a separate dockerfiles for CPUs and GPUs. The docker containers are provisioned for colorization and upscaling.


docker run -it ackimball/autocolorization-gpu /bin/bash


docker run -it ackimball/autocolorization-cpu /bin/bash


  • Caffe
  • Torch
  • ffmpeg
  • avconv
  • waifu2x
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