Awesome

IDN-Caffe

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network"

[arXiv] [CVF] [Poster] [TensorFlow version]

Testing

• Install Caffe, Matlab R2013b
• Run testing:

$ cd ./test
$ matlab
>> test_IDN

Note: Please make sure the matcaffe is complied successfully.

./test/caffemodel/IDN_x2.caffemodel, ./test/caffemodel/IDN_x3.caffmodel and ./test/caffemodel/IDN_x4.caffemodel are obtained by training the model with 291 images, and ./test/caffemodel/IDN_x4_mscoco.caffemodel is got through training the same model with mscoco dataset.

The results are stored in "results" folder, with both reconstructed images and PSNR/SSIM/IFCs.

Training

• step 1: Compile Caffe with train/include/caffe/layers/l1_loss_layer.hpp, train/src/caffe/layers/l1_loss_layer.cpp and train/src/caffe/layers/l1_loss_layer.cu
• step 2: Run data_aug.m to augment 291 dataset
• step 3: Run generate_train_IDN.m to convert training images to hdf5 file
• step 4: Run generate_test_IDN.m to convert testing images to hdf5 file for valid model during the training phase
• step 5: Run train.sh to train x2 model (Manually create directory caffemodel_x2)

Results

Set5,Set14,B100,Urban100,Manga109

With regard to the visualization of mean feature maps, you can run test_IDN first and then execute the following code in Matlab.

inspect = cell(4, 1);
for i = 1:4
    inspect{i} = net.blobs(['down' num2str(i)]).get_data();
    figure;
    imagesc(mean(inspect{i}, 3)')
end

Model Parameters

Citation

If you find IDN useful in your research, please consider citing:

@inproceedings{Hui-IDN-2018,
  title={Fast and Accurate Single Image Super-Resolution via Information Distillation Network},
  author={Hui, Zheng and Wang, Xiumei and Gao, Xinbo},
  booktitle={CVPR},
  pages = {723--731},
  year={2018}
}