Non-uniform haze removal from polarized images based on generative adversarial networks
Yang Song, En Lin, Qiuyan Yao, Hua Sun, Tao Sun · 2023
Environmental conditions with smoke severely degrade the image quality, obscuring numerous features and fine details in polarimetric images. Hence, the process of smoke removal and the recovery of polarized information in polarimetric images are of paramount importance. To address this issue, the paper proposes a novel smoke removal algorithm for polarimetric images based on Generative Adversarial Networks. It successfully restores the polarized intensity responses of four distinct polarization angles, captured by a focused plane polarimetric camera in a smoke-free environment, making the recovered images indistinguishable from those captured without smoke. Experimental validation demonstrates the superiority of the proposed method over existing classical smoke removal algorithms in terms of both subjective visual quality evaluation and objective performance metrics. The method achieves remarkable results on a natural visible light polarimetric smoke image dataset.