Image based fog density estimation.

Although the application of image-based fog density estimation brings excellent convenience and low-cost methods, the accuracy of such methods still needs to be improved, and further research is encouraged on accuracy evaluation methods. To improve the accuracy and computational efficiency of fog density estimation in images, we first construct three image features based on the image dark channel information, the image saturation information, and the proportion of gray noise points, respectively. Then, we use a feature fusion method to estimate fog density in the images. In addition, two indicators have been constructed to evaluate the accuracy of various fog density estimation methods. These two indicators are the sequential error indicator and the proportional error indicator, which are calculated using fog image sequences with known density values. These two new indicators enable the evaluation of any fog density estimation method in terms of the ability to maintain order and ratio values. The experimental results show that the proposed method can effectively estimate the fog densities of images and display the best performance among the eight latest image-based methods for estimating fog density; the three features used in the proposed method significantly impact the effectiveness of image-based fog density estimation. The proposed method has been illustrated for fog density analysis of indoor and outdoor surveillance videos. The source code is available at https://github.com/Dai-MR/ImageFogDensityEsitmation.