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README.md

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 # MultiResolutionKernelPredictionCNN
 A Multi-Resolution variant of Kernel Prediction CNN (MR-KP) denoiser
+
+We have adapted the Multi-Resolution [Kernel Prediction CNN (MR-KP) denoiser](https://dl.acm.org/doi/10.1145/3072959.3073708), which decreases the run time of a basic kernel prediction architecture to the order of tens of milliseconds (35ms on a Nvidia RTX 2080 GPU).
+![Teaser](figures/teaser-min.png)
+
+### The structure of the network
+
+![Network Structure](figures/network.png)
+
+### The structure of the pyramid-denoiser:
+
+![Multi-Resolution Denoiser Structure](figures/denoiser.png)
+
+### Citation
+If you find this implementation useful in your research, please consider citing:
+```
+@article{10.1145/3072959.3073708, 
+  author = {Bako, Steve and Vogels, Thijs and Mcwilliams, Brian and Meyer, Mark and Nov\'{a}K, Jan and Harvill, Alex and Sen, Pradeep and Derose, Tony and Rousselle, Fabrice}, 
+  title = {Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings}, 
+  year = {2017}, 
+  issue_date = {July 2017}, 
+  publisher = {Association for Computing Machinery}, 
+  address = {New York, NY, USA}, 
+  volume = {36}, 
+  number = {4}, 
+  issn = {0730-0301}, 
+  url = {https://doi.org/10.1145/3072959.3073708}, 
+  doi = {10.1145/3072959.3073708}, 
+  journal = {ACM Trans. Graph.}, 
+  month = jul, 
+  articleno = {97}, 
+  numpages = {14}, 
+  keywords = {global illumination, Monte Carlo denoising, Monte Carlo rendering} 
+}
+@inproceedings {Meng2020Real, 
+  booktitle = {Eurographics Symposium on Rendering 2020}, 
+  title = {{Real-time Monte Carlo Denoising with the Neural Bilateral Grid}}, 
+  author = {Xiaoxu Meng, Quan Zheng, Amitabh Varshney, Gurprit Singh, Matthias Zwicker}, 
+  year = {2020}, 
+  publisher = {The Eurographics Association}, 
+}
+```