Shepard Convolutional Neural Networks
Before running the code, it's a good idea to read the instructions and see the videos on the VCNN website (www.deeplearning.cc/vcnn) and setup VCNN.
You need to have a Nvidia GPU with 4GB or more GPU memory to run the code. The code runs directly in Matlab 2014b or later. You need to install CUDA before running the code. No other compilation or configuration is needed. The code was tested in both Ubuntu 14.04 and Windows 7.
Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks", Advances in Neural Information Processing Systems (NIPS 2015)
We provided the pre-trained models for 2x, 3x and 4x super-resolution.
For PSNR comparison, following previous papers in the literature, we used single channel images.
Please run this script to test 2x super-resolution.
Please run this script to test 3x super-resolution.
Please run this script to test 4x super-resolution.
Most of the methods we compared against can be found in this web page.
We also provided scripts to generate color images.
Please run this script to test 2x color image super-resolution.
Please run this script to test 3x color image super-resolution.
Please run this script to test 4x color image super-resolution.
To train a new model, run this script to generate the data. You can adjust the variable "down_factor" to indicate whether you would like to generate data for x2, x3 or x4 super-resolution. Also adjust the variable "training_mat_path" and "val_mat_path" to indicate the path to store the training and validation data.
To run examples of inpainting just run this script.