Fog-degraded images seriously affect the normal operation of the information systems of the military

traffic

safety monitoring

and other fields. Hence

research on image dehazing has become important and practical. Most existing single image dehazing approaches focus on investigating haze-relevant color features. However

different color priors have their respective limitations. To improve the adaptability of the image dehazing algorithm

we propose a single image dehazing algorithm by feature learning. First

we extract multi-scale textural and structural features from a hazy image by using a sparse auto encoder; the haze-relevant color features are obtained as well. Second

we model the scene depth with the extracted textural

structural

and color features by using multi-layer neural networks. By training samples with the neural network model

the relationship between scene depth and the features is determined

and the depth map of the hazy image is restored. Finally

the haze-free image with the depth map is restored according to the atmospheric scattering model. Compared with the results of mainstream algorithms

those of the proposed algorithm are more natural and have more details. Qualitative evaluation of the similarity between our results and the ground truth haze-free images indicates that our results have high similarity to the ground truth situation

reaching 99.9%. Experiments show that the proposed algorithm can effectively restore the depth map of a hazy image and produce a high-quality haze-free image with good adaptability.