结合高斯混合模型的改进暗通道图像去雾方法

摘要：

针对经典的暗通道先验方法在处理含有大面积天空的有雾图像时， 去雾图像的天空区域出现不同程度的颜色失真等问题， 提出了一种结合高斯混合模型的改进暗通道图像去雾方法。首先， 采用高斯混合模型对有雾图像进行建模， 然后用期望最大化（Expectation maximization, EM）算法优化模型参数， 从而将有雾图像分割成天空区域和非天空区域。 其次， 根据天空区域暗通道值的不同将其分为淡雾区、 中雾区和浓雾区， 分别估计透射率。 并结合大气散射模型得到复原图像。 最后， 采用高动态范围图像自适应局部色调映射方法提升复原图像的亮度。实验结果表明， 该方法有效地解决了经典暗通道先验方法去雾时产生的天空失真问题， 且复原后的图像更清晰、 视觉效果更好。

Abstract： To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky, an improved dark channel image dehazing method based on Gaussian mixture model is proposed. Firstly, we use the Gaussian mixture model to model the hazy image, and then use the expectation maximization (EM) algorithm to optimize the parameters, so that the hazy image can be divided into the sky region and the non-sky region. Secondly, the sky region is divided into a light haze region, a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively. Thirdly, the restored image is obtained by combining the atmospheric scattering model. Finally, adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image. The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region, and the restored image is clearer and has better visual effect.