Aiming at the problems of the current popular image dehazing algorithms, such as excessive dehazing which causes image color distortion or insufficient dehazing, an image dehazing algorithm with adaptive gamma correction estimation is proposed. Firstly, according to the brightness of the image, the different gamma correction functions are used to fit the relationship between the hazy image and the fogless image under different scene depths, and the minimum channel of fogless image is adaptively estimated. Then, the minimum channel of the non-fog image is corrected by the guided filtering algorithm to maintain the linear relationship between the hazy image and the non-fog image in the local area with the same depth of the scene. The initial transmittance according to the atmospheric scattering model is obtained. Then, the initial transmittance through Gaussian relativity is optimized to obtain the precise transmittance with structure retention and local smoothness. By increasing the search field, the upper half of the blue channel of the hazy image is used as input to improve the quadtree algorithm, and stably obtain the hazy image pixel value corresponding to the deepest part of the scene as the atmospheric light value. Finally, the brightness of the restored image is enhanced by the gamma correction function. The experimental results show that the contrast and average gradient of the restored image are increased respectively by 40.66% and 20.98% on average, and have higher information entropy. The above algorithm dehazes significantly, and the restored image has higher definition