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Single Image Dehazing Using Non-local Total Generalized Variation

机译:使用非局部总广义变化的单图像去雾

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Single image dehazing has been a challenging problem due to its ill-posed nature. In this paper, a novel single image dehazing approach is proposed to accurately model the transmission map and suppress artifacts in the recovered haze-free image. Firstly, a coarse transmission is estimated using the patch based haze-line model. After that, a non-local Total Generalized Variation regularization is introduced to refine the transmission while preserving the local smoothness property and depth discontinuities. In addition, a regularized optimization is proposed for recovering the scene radiance without bringing artifacts boosting. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results indicate that the proposed method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.
机译:由于单张图像的不适性,它已成为一个具有挑战性的问题。在本文中,提出了一种新颖的单图像去雾方法,以精确地模拟透射图并抑制恢复的无雾图像中的伪像。首先,使用基于补丁的雾度线模型估计粗略透射率。之后,引入非局部总广义变化正则化以完善传输,同时保留局部平滑性和深度不连续性。另外,提出了一种规整化的优化,用于在不增加伪影的情况下恢复场景的辐射度。与最新的除雾方法相比,定量和定性的实验结果均表明,该方法能够获得准确的透射图和可视化的除雾图像。

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