/ Machine Learning

Parallax Attention Stereo Super-Resolution Network

Parallax Attention Stereo Super-Resolution Network

PASSRnet

Pytorch implementation of "Learning Parallax Attention for Stereo Image Super-Resolution", CVPR 2019

Figure 1. Overview of our PASSRnet network.

Parallax-attention

Figure 2. Illustration of our parallax-attention mechanism.

Toy-example

Figure 3. A toy example illustration of the parallax-attention and cycle-attention maps generated by our PAM.
The attention maps (30×30) correspond to the regions (1×30) marked by a yellow stroke. In (a) and (b), the first row
represents left/right stereo images, the second row stands for parallax-attention maps, and the last row represents cycle-attention maps.

Flickr1024 Dataset

Flickr1024

Figure 4. The Flickr1024 dataset.

Requirements

  • pytorch (0.4), torchvision (0.2) (Note: The code is tested with python=3.6, cuda=9.0)
  • Matlab (For training/test data generation)

Train

Prepare training data

  1. Download the Flickr1024 dataset and put the images in data/train/Flickr1024
    (Note: In our paper, we also use 60 images in the Middlebury dataset as the training set.)
  2. Cd to data/train and run generate_trainset.m to generate training data.

Begin to train

python train.py --scale_factor 4 --device cuda:0 --batch_size 32 --n_epochs 80 --n_steps 30

Test

Prepare test data

  1. Download the KITTI2012 dataset and put folders testing/colored_0 and testing/colored_1 in data/test/KITTI2012/original
  2. Cd to data/test and run generate_testset.m to generate test data.
  3. (optional) You can also download KITTI2015, Middlebury or other stereo datasets and prepare test data in data/test as below:
  data
  └── test
      ├── dataset_1
            ├── hr
                ├── scene_1
                      ├── hr0.png
                      └── hr1.png
                ├── ...
                └── scene_M
            └── lr_x4
                ├── scene_1
                      ├── lr0.png
                      └── lr1.png
                ├── ...
                └── scene_M
      ├── ...
      └── dataset_N

Demo

python demo_test.py --scale_factor 4 --device cuda:0 --dataset KITTI2012

Results

results_2x_KITTI2012_KITTI2015

Figure 5. Visual comparison for 2× SR. These results are achieved on “test_image_013” of the KITTI 2012 dataset and “test_image_019” of the KITTI 2015 dataset.

results_4x_KITTI2015

Figure 6. Visual comparison for 4× SR. These results are achieved on “test_image_004” of the KITTI 2015 dataset.

results_2x_lab

Figure 7. Visual comparison for 2× SR. These results are achieved on a stereo image pair acquired in our laboratory.

Citation

@InProceedings{2018-LearningforVideoSuperResolutionthroughHROpticalFlowEstimation-LongguangWang--,
  author    = {Longguang Wang and Yingqian Wang and Zhengfa Liang and Zaiping Lin and Jungang Yang and Wei An and Yulan Guo},
  title     = {Learning Parallax Attention for Stereo Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2019},
}

Contact

For questions, please send an email to [email protected]

GitHub