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An adaptive image dehazing algorithm based on dark channel prior

机译:基于暗通道先验的自适应图像去雾算法

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Traditional dehazing algorithm based on dark channel prior may suffer weak robustness against the variation of hazy weather and may fail in bright regions. To resolve these issues, this paper proposes an improved adaptive dehazing algorithm based on dark channel prior. Our method can adaptively calculate dehazing parameter, such as the degree of haze removal. Here the dehazing parameters are local, rather than global variables. We compute the local dehazing parameter automatically according to haze distribution, which makes our method being able to handle different dehazing degrees under various weather conditions, and makes haze removal more robust. We also propose a new method to optimize the rough transmission paramters, which can help to remove the distortion in bright regions. Experiments confirm the advantages of our method, such as robustness against different scenes, high color fidelity of the restored images and greatly enhanced details of the hazy regions.
机译:基于暗信道先验的传统除雾算法可能会遇到雾霾天气变化的鲁棒性较弱,并且在明亮区域可能会失败。为了解决这些问题,本文提出了一种改进的基于暗信道先验的自适应去雾算法。我们的方法可以自适应地计算除雾参数,例如除雾度。这里的除雾参数是局部变量,而不是全局变量。我们根据雾度分布自动计算局部除雾参数,这使我们的方法能够在各种天气条件下处理不同的除雾度,并使除雾更加稳健。我们还提出了一种优化粗略透射参数的新方法,该方法可以帮助消除明亮区域的失真。实验证实了我们方法的优势,例如针对不同场景的鲁棒性,还原图像的高色彩保真度以及模糊区域的细节大大增强。

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