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. I...
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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 robustdehazingsegmentationscheme. 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.
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