PyTorch implementation of the algorithm presented in our paper [1]. The algorithm can be used to perform guided super-resolution, for instance:

Have a look at our blog post or publication if you want to learn more about the method.

The function PixTransform takes as input two images, the source image of size M x M and a guide image of size C x N x N, and returns an upsampled version of the source image with size N x N. The upsampling factor D, equal to N/M, must be an integer.

predicted_target = PixTransform(source,guide)

additional variables can be passed to change the default parameters. For further details about the algorithm see [1]

clone this git repository and make sure that the following packages are installed:

• numpy
• matplotlib
• scipy
• Pytorch
• ProxTV (optional)
• tqdm

To run the algorithm on some sample images check the Jupyter Notebook file process_examples.ipynb.

We compare the results of our algorithm with the results of the guided filter [2], the fast bilateral solver [3] (authors' implementation), the static-dynamic filter [4] (authors' implementation) and the multi-scale guided network [5] (authors' implementation).

[1] R. de Lutio, S. D'Aronco, J. D. Wegner, K. Schindler. "Guided Super-Resolution as Pixel-to-Pixel Transformation", ICCV, 2019.

[2] K. He, J. Sun, X. Tang. "Guided image filtering", TPAMI, 2013.

[3] J. T. Barron, B. Poole. "The fast bilateral solver", ECCV, 2016.

[4] B. Ham, M. Cho, J. Ponce. "Robust guided image filtering using nonconvex potentials", TPAMI, 2018.

[5] T.-W. Hui, C. C. Loy, X. Tang. "Depth map super-resolution by deep multi-scale guidance", ECCV, 2016.