Gaussian Adaptive Standard Deviation Dehazing Algorithm Under Linear Constraint

Aiming at the problem that the transmission of dark channel prior algorithm is too small in bright areas such as the sky area and the minimum filter is insufficient,a Gaussian adaptive standard deviation dehazing algorithm under linear constraint is proposed.Constructing a Gaussian function by using the minimum channel map of the hazy image to approximate the minimum channel effect of the haze-free image,thereby improving the accuracy of transmission in bright areas such as the sky area.In order to prevent the gray level of minimum channel of the haze-free image from exceeding the range,a linear coefficient is proposed to constraint so that the gray level is distributed within(0,1).Secondly,it is observed that the standard deviation of Gaussian function is negatively correlated with haze concentration,so that an adaptive standard deviation is proposed to control the final restoration effect,and optimized by cross-bilateral filtering.Finally,the hazy-free image is restored by combining the atmospheric scattering model.The simulation experiments were carried out in Matlab R2014a software,and this algorithm were verified by visual effects analysis and blind evaluation indicators.Numerous experimental results show that compared with the classical algorithm,this algorithm effectively recovers the contrast and detail information of the hazy scene,which obviously increase the image visibility and has lower time complexity.