Polarimetric dehazing is an effective way to enhance the quality of images captured in foggy weather. However, images of essential polarization parameters are vulnerable to noise, and the brightness of dehazed images is usually unstable due to different environmental illuminations. These two weaknesses reveal that current polarimetric dehazing algorithms are not robust enough to deal with different scenarios. This paper proposes a novel, to our knowledge, and robust polarimetric dehazing algorithm to enhance the quality of hazy images, where a low-rank approximation method is used to obtain low-noise polarization parameter images. Besides, in order to improve the brightness stability of the dehazed image and thus keep the image have more details within the standard dynamic range, this study proposes a multiple virtual-exposure fusion (MVEF) scheme to process the dehazed image (usually having a high dynamic range) obtained through polarimetric dehazing. Comparative experiments show that the proposed dehazing algorithm is robust and effective, which can significantly improve overall quality of hazy images captured under different environments.

中文翻译：

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基于低秩近似和多重虚拟曝光融合的鲁棒偏振去雾算法


偏振去雾是提高雾天拍摄图像质量的有效方法。然而，基本偏振参数的图像容易受到噪声的影响，并且去雾图像的亮度通常会因环境​​光照的不同而不稳定。这两个弱点表明，当前的偏振去雾算法不足以应对不同的场景。据我们所知，本文提出了一种新颖且鲁棒的偏振去雾算法来提高模糊图像的质量，其中使用低秩近似方法来获得低噪声偏振参数图像。此外，为了提高去雾图像的亮度稳定性，从而使图像在标准动态范围内具有更多的细节，本研究提出了一种多重虚拟曝光融合（MVEF）方案来处理去雾图像（通常具有较高的对比度）。动态范围）通过偏振去雾获得。对比实验表明，所提出的去雾算法稳健有效，可以显着提高不同环境下拍摄的有雾图像的整体质量。