In view of the problems of low brightness and obvious color distortion of the sky in restored images in most existing algorithms for image dehazing, a haze removal method for UAV aerial images based on atmospheric light value and graph estimation was proposed. First, the depth-of-field image was obtained according to the color attenuation prior theory, and the mean value of the region with the minimum deviation in the depth-of-field image was taken as the atmospheric light value. Then, a random walk clustering method was designed to estimate the atmospheric light map. The random walk algorithm was used to cluster the image into N sub-regions, and the mean value of the first 0.1% pixels of the sub-regions was taken as the regional atmospheric light value, which was then combined and refined by guided filtering to obtain the atmospheric light map. Next, the two atmospheric light estimators were fused into a new atmospheric light map with atmospheric light valuegraph estimation, which is a more accurate atmospheric light estimator. The transmittance was obtained by haze-lines prior method, and a dark compensation method was proposed to improve the transmission accuracy. Finally, according to the atmospheric scattering model, a clear restored image was obtained based on the fused atmospheric light map and optimized transmittance. Experimental results show that compared with other algorithms, the proposed algorithm improved the information entropy, mean gradient, blur coefficient, and contrast by 1.1%, 6.3%, 8.5%, and 6.4%, respectively, with better subjective visual effect and more abundant information.