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WSWTNN-PnP:Combining Deep Denoiser and Low-rank Priors for Infrared Small Target Detection

<p align="center"> <img src="https://raw.github.com/LiuTing20a/WSWTNN-PnP1/master/Figs1/1.png" width="90%"> </p>

Matlab implementation of "Combining Deep Denoiser and Low-rank Priors for Infrared Small Target Detection''.

Highlights:

Preparation:

1. Requirement:

Test on the data:

The code for compared methods

Results:

To demonstrate the advantages of the WSWTNN-PnP method, we compare it with other ten methods on six different real infrared image scenes.

Visual Comparisons:

<p align="center"> <img src="https://raw.github.com/LiuTing20a/WSWTNN-PnP1/master/Figs1/2.png" width="90%"> </p> <p align="center"> <img src="https://raw.github.com/LiuTing20a/WSWTNN-PnP1/master/Figs1/3.png" width="90%"> </p>

Ablation Experiments

<p align="center"> <img src="https://raw.github.com/LiuTing20a/WSWTNN-PnP1/master/Figs1/4.jpg" width="90%"> </p>

Details

For details such as parameter setting, please refer to [<a href="https://doi.org/10.1016/j.patcog.2022.109184">pdf</a>].

Citation

@article{liu2022combining,
  title={Combining Deep Denoiser and Low-rank Priors for Infrared Small Target Detection},
  author={Liu, Ting and Yin, Qian and Yang, Jungang and Wang, Yingqian and An, Wei},
  journal={Pattern Recognition},
  pages={109184},
  year={2022},
  publisher={Elsevier}
}

Contact

Welcome to raise issues or email to liuting@nudt.edu.cn for any question regarding this work.