Abstract: Defogging algorithms based on atmospheric model always had atmospheric light and medium transmission limited by statistical or hypothetical information.Hence a non-hypothetical parameter estimation method was proposed.For precisely acquiring these parameters,the atmospheric light was solved by a quad-tree algorithm firstly.Secondly,a pre-trained convolutional neural network was proposed for estimating the transmission map optimized by the guided filtering algorithm further.Finally,by reversely solving the atmospheric scattering model,the de-fogging image was obtained.Experiments show that the proposed method has balanced performance on each index.It not only improves the degree of foggy image definition and brightness,but also efficiently avoids the Halo effect.Time performance also analysis indicates that,compared to other defogging algorithms,efficiency of our algorithm using CPU has increased 40%at least.After parallelizing the time-consuming guided filtering algorithm through CUDA,the efficiency has improved remarkably which can process a fog image with the resolution of 640×480 pixels only in 0.048 9 s.It can be directly applied to video processing to meet real-time requirement.