Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de-hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post-processing towards human assistance systems benefit. In this study, the authors present a variational segmentation model equipped with de-hazing constraint terms in a new coupled dehazing-segmentation model. The proposed hybrid formulation not only recovers/restores the fog/haze degradation but at the same time segments image degraded object/objects by solving in this way the difficulties of simultaneously performed dehazing and segmentation pre/post-processing. This combination takes into account the image structure boundaries and the image quality, leading in this way to a robust dehazing segmentation scheme. The advantages of the proposed method are the suitability of the model for grey and vector-valued images, a small number of parameters involved, and a rather good speed of the algorithm. Experiments show that their approach outperforms the state-of-the-art algorithms in terms of segmentation accuracy while avoiding a dehazing preprocessing which reflects an extended CPU time.

中文翻译：

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具有消雾约束的主动轮廓图像分割模型

在朦胧或有雾的天气条件下捕获的图像可能会由于大气颗粒的散射而严重退化，这使得物体及其特征很难被计算机视觉系统识别。在过去的几十年中，通过提供易于人为帮助的图像后处理功能，图像去雾化用于消除天气因素的影响并改善朦胧场景中的图像可视化效果。在这项研究中，作者在新的耦合除雾分割模型中提出了一种带有除雾约束项的变分分割模型。通过以这种方式解决同时执行除雾和分割前/后处理的困难，提出的混合配方不仅恢复/恢复了雾/雾的退化，而且同时分割了图像退化的一个或多个物体。这种组合考虑了图像结构的边界和图像质量，从而导致了鲁棒的除雾分割方案。该方法的优点是该模型对灰度和矢量图像的适用性，涉及的参数数量少以及算法的速度较好。实验表明，在分割精度方面，他们的方法优于最新的算法，同时避免了会延长CPU时间的除雾预处理。