Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution

Pytorch implementation of local motion and contrast prior driven deep network (MoCoPnet). [PDF]

Overview

Requirements

• Python 3
• pytorch >= 1.6
• numpy, PIL

Datasets

Training & test datasets

Download SAITD dataset.

SAITD dataset is a large-scale high-quality semi-synthetic dataset of infrared small target. We employ the 1st-50th sequences with target annotations as the test datasets and the remaining 300 sequences as the training datasets.

Download Hui and Anti-UAV.

Hui and Anti-UAV datasets are used as the test datasets to test the robustness of our MoCoPnet to real scenes. In Anti-UAV dataset, only the sequences with infrared small target (i.e., The target size is less than 0.12% of the image size) are selected as the test set (21 sequences in total). Note that, we only use the first 100 images of each sequence for test to balance computational/time cost and generalization performance.

For simplicity, you can also Download the test datasets in https://pan.baidu.com/s/1oobhklwIChvNJIBpTcdQRQ?pwd=1113 and put the folder in code/data.

Data format:

1. The training dataset is in code/data/train/SAITD.

train
  └── SAITD
       └── 1
              ├── 0.png
              ├── 1.png
              ├── ...
       └── 2
              ├── 00001
              ├── 00002
              ├── ...		
       ...

2. The test datasets are in code/data/test as below:

 test
  └── dataset_1
         └── scene_1
              ├── 0.png  
              ├── 1.png  
              ├── ...
              └── 100.png    
               
         ├── ...		  
         └── scene_M
  ├── ...    
  └── dataset_N      

Results

Quantitative Results of SR performance

Qualitative Results of SR performance

Quantitative Results of detection

Qualitative Results of detection

Citiation

@article{MoCoPnet,
  author = {Ying, Xinyi and Wang, Yingqian and Wang, Longguang and Sheng, Weidong and Liu, Li and Lin, Zaiping and Zhou, Shilin},
  title = {Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution},
  journal={Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2022},
}

Contact

Please contact us at [email protected] for any question.