Haze degrades the spatial and spectral information of outdoor images. It may reduce the performance of the existing imaging models. Therefore, various visibility restoration models approaches have been designed to restore haze from still images. But restoring the haze is an open area of research. Although the existing approaches perform significantly better, they are not so effective against a large haze gradient. Also, the effect of hyperparameters tuning issue is also ignored. Therefore, a brightness channel prior (BCP) based dehazing model is proposed. The gradient filter is utilized to improve the transmission map computed using the gradient filter. Nondominated Sorting Genetic Algorithm is also used to optimize the initial parameters of the BCP approach. The comparative analysis shows that BCP performs effectively across a wide range of haze degradation levels without causing any visible artifacts.

中文翻译：

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基于元启发式亮度通道先验的高效单图像去雾模型

雾度降低了室外图像的空间和光谱信息。它可能会降低现有成像模型的性能。因此，已经设计了各种可见度恢复模型方法来从静止图像恢复雾度。但是恢复雾霾是一个开放的研究领域。尽管现有方法的性能要好得多，但对于大雾度梯度却不是那么有效。此外，也忽略了超参数调整问题的影响。因此，提出了一种基于亮度通道先验（BCP）的除雾模型。利用梯度滤波器来改善使用梯度滤波器计算出的透射图。非支配排序遗传算法也用于优化BCP方法的初始参数。