Fang Shuai, Xia XiuShan, Xing Huo, Chen ChangWen
Opt Express. 2014 Aug 11;22(16):19523-37. doi: 10.1364/OE.22.019523.
The analysis of polarized filtered images has been proven useful in image dehazing. However, the current polarization-based dehazing algorithms are based on the assumption that the polarization is only associated with the airlight. This assumption does not hold up well in practice since both object radiance and airlight contribute to the polarization. In this study, a new polarization hazy imaging model is presented, which considers the joint polarization effects of the airlight and the object radiance in the imaging process. In addition, an effective method to synthesize the optimal polarized-difference (PD) image is introduced. Then, a decorrelation-based scheme is proposed to estimate the degree of polarization for the object from the polarized image input. After that, the haze-free image can be recovered based on the new polarization hazy imaging model. The qualitative and quantitative experimental results verify the effectiveness of this new dehazing scheme. As a by-product, this scheme also provides additional polarization properties of the objects in the image, which can be used in extended applications, such as scene segmentation and object recognition.
偏振滤波图像分析已被证明在图像去雾中很有用。然而，当前基于偏振的去雾算法基于这样一种假设，即偏振仅与大气光相关。这种假设在实际中并不成立，因为物体辐射和大气光都对偏振有贡献。在本研究中，提出了一种新的偏振模糊成像模型，该模型考虑了成像过程中大气光和物体辐射的联合偏振效应。此外，引入了一种合成最优偏振差（PD）图像的有效方法。然后，提出了一种基于去相关的方案，从输入的偏振图像中估计物体的偏振度。在此之后，可以基于新的偏振模糊成像模型恢复无雾图像。定性和定量实验结果验证了这种新去雾方案的有效性。作为一个副产品，该方案还提供了图像中物体的额外偏振特性，可用于扩展应用，如场景分割和目标识别。
