This is the implementation of SIGGRAPH Asia 2018 paper "Image Smoothing via Unsupervised Learning" by Qingnan Fan, et al.

Our work is the first unsupervised learning approach for the image smoothing problem. Our proposed objective function can be slightly modifed to achieve different image processing effects, for example,

Edge-preserving smoothing Image abstraction Detail enhancement Background smooth Foreground enhancement

This paper is implemented with Torch framework.

Our codes are implemented in Torch framework. Some self-defined layers are included in the 'compilation' folder. Before testing or training the models, you need to install the latest torch framework and compile the *.lua under the nn module, the *.cu and *.h under the cunn module, and adam_state.lua under optim module.

To be specific, the lua files have to be put in ./torch/extra/nn/ module directory, and editing init.lua file to include the corresponding file. Finally run the command under nn directory, luarocks make ./rocks/nn-scm-1.rockspec. Then nn module will be independently compiled.

Similarly for the cuda files, they need to be put into ./torch/extra/cunn/lib/THCUNN/ module directory, then editing the ./torch/extra/cunn/lib/THCUNN/generic/THCUNN.h to include the corresponding files, and finally run the command luarocks make ./rocks/cunn-scm-1.rockspec under ./torch/extra/cunn folder to compile them.

Accordingly, the adam lua file has to be put in ./torch/pkg/optim, edit the init.lua file and then run luarocks make optim-1.0.5-0.rockspec.

Run run_edgedetector.lua to compute the edges for edge-preserving smoothing or texture removal. To learn the content-aware image smoothing effects, we need saliency map for each training sample, which could be generated by existing saliency prediction methods.

The trained models are all in the netfiles folder.

Run train_*.lua to learn specific smoothing effects.

To test our trained model, run test_smooth.lua.

For the application of stylization, run post_stylization.m after predicting the smooth images. Similarly, for detail enhancement, run post_detail_enhance.m.

You can use our codes for research purpose only. And please cite our paper when you use our codes.

@Article{fan2018smoothing,
Title = {Image Smoothing via Unsupervised Learning},
Author = {Qingnan Fan and Jiaolong Yang and David Wipf and Baoquan Chen and Xin Tong},
Journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH ASIA 2018)},
Year = {2018},
Volume = {37}
Number = {6},
}

If you find any bugs or have any ideas of optimizing these codes, please contact me via fqnchina [at] gmail [dot] com