Fast Image Processing with Fully-Convolutional Networks
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readme.MD

Fast Image Processing with Fully-Convolutional Networks

This is a Tensorflow implementation of Fast Image Processing with Fully-Convolutional Networks.

Demo Video

https://www.youtube.com/watch?v=eQyfHgLx8Dc

Setup

Requirement

Required python libraries: Tensorflow (>=1.0) + Opencv + Numpy.

Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes.

Quick Start (Testing)

  1. Clone this repository.
  2. Run "CAN24_AN/demo.py". This will generate results on L0 smoothing in "CAN24_AN/L0_smoothing/MIT-Adobe_test_1080p_result".
  3. To test a different model, change the variable "task" in "demo.py"

Training

  1. To train, change "is_training" to "True".
  2. To set up a customized training procedure, change the file paths in "prepare_data()". See the commands in the code.

Extensions

  1. The single network for all operators is "combined.py" in the folder "Single_Network". Run it and its result is in "Single_Network/result_combined/video".
  2. The parameterized network is "parameterized.py" in the folder "Parameterized_Network". Run it and its result is in "Parameterized/result_parameterized/video".

Data

If you want to experiment on the data in our evaluation, please email to chenqifeng22@gmail.com.

Citation

If you use our code for research, please cite our paper:

Qifeng Chen, Jia Xu, and Vladlen Koltun. Fast Image Processing with Fully-Convolutional Networks. In ICCV 2017.

License

MIT License.