To improve the recovery ability of polarization dehazing algorithms in fog scenes， a polarization image dehazing algorithm based on polarization optimization and atmospheric light correction is proposed. First， according to the brightness distribution of the fog scene， the fog image was decomposed into bright residuals and dark residuals via guided filtering. Second， to optimize the degree of polarization， the degrees of polarization corresponding to the bright and dark residuals were increased and decreased， respectively. This optimized degree of polarization can blur the atmospheric light image. The difference value of the degree of polarization in the residuals was used to correct the atmospheric light for ensuring its intensity range met the atmospheric degradation model. Experiments indicated that the contrast ratio was 3.07 times that in original hazy images after dehazing and that the entropy and standard deviation of dehazed images were increased by 9.21% and 61.86%， respectively. In environments with different concentrations of simulated fog， the proposed algorithm achieved excellent SSIM， CIEDE2000， and PSNR values. Compared with the state-of-art dehazing algorithms， the effect of the proposed algorithm was obvious， and it recovered the scene details efficiently.