This technical report briefly introduces to the D$^{3}$Net 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.
翻译：本技术报告简要介绍了由我们的“TUK-IKLAB”团队提议的“2022年CVPR大气扰动挑战”“TUK-IKLAB”项目“D$3}”网络。根据改进文本识别性能的文本图像测试和验证结果,以及提高图像的热气球图像,我们可以说,拟议方法达到了最新性能。此外,我们还提供了与拟议工作有关的公开拆卸、拆卸和平均法框架的目视比较。拟议方法在测试阶段分别排在上述挑战的最后领导板的第二位。
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