A dehazing method often only shows good results when processing the image for a certain haze concentration. So an adaptive hazy image dehazing method based on SVM is proposed. The innovation points are as follows: Firstly, combining the characteristics of the degraded images of haze weather, the dark channel histogram and texture features of the input images are extracted to form the feature vectors. These are trained by supervised learning through SVM algorithm to realize automatic binary classification of images; Secondly, the defined dehazing methods are called to process the classified result as a hazy image and the same quality evaluation indexes are used to evaluate each image output by different dehazing methods. Then, it outputs the highest evaluation image after haze removal. Finally, the output image is classified again by SVM until the image reaches the clearest it can be. The experimental results show that the proposed algorithm exhibits good contrast, brightness and color saturation from the visual effect. Also the scene adaptability and robustness of the algorithm are improved.

当以一定的雾度浓度处理图像时，除雾方法通常只会显示出良好的效果。因此，提出了一种基于支持向量机的自适应模糊图像去雾方法。创新点如下：首先结合雾霾天气退化图像的特征，提取输入图像的暗通道直方图和纹理特征，形成特征向量。这些都是通过SVM算法的监督学习训练的，以实现图像的自动二进制分类；其次，调用定义的除雾方法将分类结果处理为雾图像，并使用相同的质量评估指标评估通过不同除雾方法输出的每个图像。然后，在除雾后输出最高评价图像。最后，SVM再次对输出图像进行分类，直到图像达到最清晰为止。实验结果表明，该算法在视觉效果上具有良好的对比度，亮度和色彩饱和度。同时提高了算法的场景适应性和鲁棒性。