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Variational Histogram Equalization for Single Color Image Defogging

机译：单色图像去雾的变分直方图均衡

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摘要

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.
机译：在恶劣天气下拍摄的雾图像不可避免地会遭受对比度损失和色彩失真。现有的除雾方法仅在不了解其令人不快的失真和高复杂度的情况下仅求出准确的场景传输。与以前的工作不同，我们提出一种基于直方图均衡化和物理退化模型的简单但功能强大的方法。通过修改变化直方图均衡框架中的两个约束，可以在HSI颜色空间中估计无雾图像的强度分量，因为预先通过颜色衰减来推断出光线。为了减少时间消耗，提出了一种通用变化滤波器来从修改后的框架中获得数值解。在获得估计的强度分量后，很容易从饱和度通道中的物理退化模型推断出饱和度分量。因此，可以用估计的强度和饱和度分量恢复无雾图像。最后，该方法在多幅模糊图像上进行了测试，并通过两个无参考指标进行了评估。实验结果表明，我们的方法相对优于三组相关的和最新的除雾方法。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|9897064.1-9897064.17|共17页
  • 作者

    Zhou Li; Bi Du Yan; He Lin Yuan;

  • 作者单位

    Air Force Engn Univ, Commun & Nav Lab, Aerosp Engn Coll, Xian 710038, Peoples R China;

    Air Force Engn Univ, Commun & Nav Lab, Aerosp Engn Coll, Xian 710038, Peoples R China;

    Air Force Engn Univ, Commun & Nav Lab, Aerosp Engn Coll, Xian 710038, Peoples R China;

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