Awal SHER, Haider ALI, Nosheen ZIKRIA, Lavdie RADA

A joint image dehazing and segmentation model

Objects and their feature identification in hazy or foggy weather conditions has been of interest in the lastdecades. Improving image visualization by removing weather influence factors for easy image postprocessing, such asobject detection, has benefits for human assistance systems. In this paper, we propose a novel variational model thatwill be capable of jointly segmenting and dehazing a given image. The proposed model incorporates atmospheric veilestimation and locally computed denoising constrained surfaces into a level set function by performing a robust andefficient image dehazing and segmentation scheme for both gray and color outdoor images. The proposed model notonly shows efficient segmentation of objects in foggy images by outperforming state-of-the-art methods but also producesdehazed object results in the same time.

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