In non-uniform haze scenarios, current dehazing algorithms often lack the ability to dynamically handle the non-uniform distribution of haze concentration, leading to the problem of residual haze that is difficult to effectively resolve. To address this problem, a double iteration separation dehazing algorithm for polarized images under non-uniform concentration is proposed. This algorithm mainly consists of two modules: concentration estimation and optimization of the double iteration separation algorithm. First, the polarization characteristics are used to construct the dynamic haze concentration function in the image. Then, the atmospheric scattering coefficient is obtained on the basis of the haze concentration function, and the atmospheric scattering coefficient is substituted into the double iterative separation algorithm to solve the optimal transmittance. Finally, the image is restored by combining the atmospheric light value at infinity estimated by the polarization difference image. Experimental results show that compared with CAP, BCCR, ZSR, PLE, BSMP, and other dehazing algorithms, the natural image quality evaluation index, fog concentration evaluation index, and mean gradient of the proposed algorithm are increased by 1.26%, 23.78%, and 75.63%, respectively, and the proposed algorithm achieves better dehazing effect for different concentration regions, and the haze residue is further reduced.