Dehazing Algorithm By Wavelet Fusion Based On Color Attenuation Prior

Human production and life have badly effect on the natural environment,which leading to fog haze weather appears frequently.The pictures taken under foggy weather are blurred.Haze images affect the use of various video surveillance systems.Therefore,to recover clear images is now a hot research topic and also a topic full of practical value.Single image haze removal using color attenuation prior is a new method proposed in recent years.The method is fast and effective,and greatly improves the speed of fog removal,and has promotional value.However in the algorithm,the choice of atmospheric scattering coefficients has a great influence on the final fog removal results and manual adjustment is required.And color attenuation prior works badly in thick fog area so that this algorithm can not restore a clear image when there is thick fog in the image.In this thesis,these two problems have been improved.A single image dehazing algorithm by wavelet fusion based on color attenuation prior is proposed in this thesis.First of all,a linear model of relationship between transmission and brightness and saturation is built.Secondly,400 images and their accurate depth of field information are used to generate the training samples.The probability distribution of atmospheric scattering coefficients is directly incorporated into the training samples,which guarantees the accuracy of training samples.And the supervised learning algorithm in machine learning is used to estimate the image transmission.Then the wavelet fusion algorithm is used to fuse the estimated transmittance with theinverse image of the grayscale image.Finally,the fog is processed by the optimized transmittance information.Making a model of transmittance can avoid the choice of atmospheric scattering coefficient.The transmittance refined by the inverse image of the grayscale image can improve the accuracy of transmittance.Improve the performance of this algorithm in thick fog area.Experimental results show that the algorithm proposed in this thesis is feasible and effective.At the end of the thesis,several common quality evaluation methods of dehaze are used to compare the performance of the algorithm in the thesis and the classical algorithms.The experimental results show that the algorithm can achieve better results for all kinds of fog images with a short time.The main innovations of this thesis are summarized as follows:(1)A linear model of transmittance about image's brightness and saturation is built which avoids the manually selecting of the atmospheric scattering factor.(2)The histogram of the atmospheric scattering coefficient is obtained and put into the training samples,which helps to obtain training samples with high accuracy and ensures the reliability of the model.(3)The inverse image of the gray scale image can be used as the transmittance to haze removal.And it works well in thick fog area.(4)The wavelet transform is used to fuse the inverse information of the gray scale image and the rough depth map to improve the accuracy of the transmission and ensure the quality of haze removal.