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Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

机译:基于黑暗通道的自适应公差脱落算法

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摘要

The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.
机译:当朦胧图像中存在大面积的亮区域的大面积时,基于暗信道的公差机制不太有效,因为它不能根据图像的特征自适应地获得公差。它将导致图像传输的改善不足,因此很难完全消除恢复图像中的颜色失真和块效果。此外,当密集的雾度区域或第三方直接光源(阳光,前灯和反射眩光)被判定为天空区域时,耐受性的使用将导致诸如细节丢失的劣化效果。关于上述问题,本文提出了一种自适应公差估计算法。根据每个图像的统计特性获得公差,以更准确地估计变速器。实验结果表明,该算法不仅保持高运行效率,而且还有效地补偿了在某些场景之前的黑暗信道的缺陷。所提出的算法可以有效地解决了图像的明亮区域中的DCP方法恢复的颜色失真问题。

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