2nd Place Solutions for UG2+ Challenge 2022 -- D3Net for Mitigating Atmospheric Turbulence from Images
Not on the map. Why?
Abstract (arXiv)
This technical report briefly introduces to the D3Net proposed by our team "TUK-IKLAB" for Atmospheric Turbulence Mitigation in 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 25 Aug 2022.
Builds on
5 of 8 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 470
- 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
- 2021Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transformcited by 50
- 2022NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Resultscited by 20
Built on by
Not cited yet.