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由于煤矿井下环境恶劣,存在大量的粉尘、水雾,使得煤矿井下视频监控获取的图像严重降质,而现有的基于暗通道先验的尘雾清晰化算法在处理煤矿尘雾图像时存在局限性,因此提出了一种改进的基于负片修正的尘雾图像清晰化算法。针对原有算法产生的严重的光环效应,通过建立参数间的映射实现了修正参数的精细化,从而有效的抑制了光环效应的产生。考虑获取的复原图像亮度比较低,对其进行伽马校正并获得最终清晰化图像。与其他算法相比,该算法能够有效的对尘雾图像进行清晰化复原,使得复原图像色彩更加饱和、信息量更加丰富,展现了该算法的优越性。
Due to the harsh environment in the coal mine, a large amount of dust and water fog exist, which seriously degrades the images captured by the video surveillance in underground coal mines. However, the existing dust fog clearing algorithm based on the dark channel prioritization has limitations in dealing with coal dust images. Therefore, an improved dust fog image sharpening algorithm based on negative correction is proposed. Aiming at the severe halo effect caused by the original algorithm, the refinement of the correction parameters is achieved by establishing the mapping between the parameters, thereby effectively suppressing the halo effect. The restored image considered for acquisition has a relatively low brightness, is gamma-corrected and the final sharpened image is obtained. Compared with other algorithms, this algorithm can effectively restore the dust fog images, make the restored images more saturated in color and more abundant in information, and show the superiority of this algorithm.