An efficient estimation of transmission map for desmogging model is an ill-posed problem. The quality of restored image depends upon the accurate estimation of transmission map. However, transmission map obtained using various dehazing models is not accurate in case of images with large haze gradient, and fail while image desmogging. As a result, the restored images suffer from numerous issues such as halo and gradient reversal artefacts, edge and texture distortion, color distortion, etc. Therefore, this paper designs a novel transmission map estimation by using weighted integrated transmission maps obtained from foreground and sky regions. Additionally, transmission map is further refined using an integrated variational regularized model with hybrid constraints. However, the proposed technique suffers from hyper-parameters tuning issue, therefore, in this paper, a non-dominated sorting genetic algorithm is also used to tune the hyper-parameters of the proposed technique. The comparison of designed desmogging model is also done with other dehazing models by considering benchmark and real-time hazy images. The comparative analyses reveal that the designed model outperforms existing models subjectively and quantitatively.

中文翻译：

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使用倾斜梯度轮廓先验和变分最小化的单幅图像去雾

除雾模型的透射图的有效估计是不适定问题。恢复图像的质量取决于对透射图的准确估计。然而，使用各种去雾模型获得的透射图在雾度梯度大的图像情况下不准确，并且在图像去雾时失败。因此，恢复的图像存在许多问题，例如光晕和梯度反转伪影、边缘和纹理失真、颜色失真等。因此，本文设计了一种新的透射图估计，利用从前景和天空获得的加权集成透射图地区。此外，使用具有混合约束的集成变分正则化模型进一步细化传输图。然而，所提出的技术存在超参数调整问题，因此，在本文中，还使用了非支配排序遗传算法来调整所提出技术的超参数。通过考虑基准和实时模糊图像，还对设计的去雾模型与其他去雾模型进行了比较。比较分析表明，设计的模型在主观上和数量上都优于现有模型。