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2nd Place Solutions for UG2+ Challenge 2022 -- D$^{3}$Net for Mitigating Atmospheric Turbulence from Images

Computer Vision and Pattern Recognition 2022-08-29 v1 Machine Learning Image and Video Processing

Abstract

This technical report briefly introduces to the D3^{3}Net proposed by our team "TUK-IKLAB" for Atmospheric Turbulence Mitigation in UG2+UG2^{+} Challenge at CVPR 2022. In the light of test and validation results on textual images to improve text recognition performance and hot-air balloon images for image enhancement, we can say that the proposed method achieves state-of-the-art performance. Furthermore, we also provide a visual comparison with publicly available denoising, deblurring, and frame averaging methods with respect to the proposed work. The proposed method ranked 2nd on the final leader-board of the aforementioned challenge in the testing phase, respectively.

Keywords

Cite

@article{arxiv.2208.12332,
  title  = {2nd Place Solutions for UG2+ Challenge 2022 -- D$^{3}$Net for Mitigating Atmospheric Turbulence from Images},
  author = {Sunder Ali Khowaja and Ik Hyun Lee and Jiseok Yoon},
  journal= {arXiv preprint arXiv:2208.12332},
  year   = {2022}
}

Comments

4 pages, 4 figures