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Fast Image Dehazing Method Based on Linear Transformation

机译:基于线性变换的快速图像去雾方法

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Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.
机译:在朦胧或有雾的天气条件下捕获的图像会由于大气颗粒的散射而严重劣化,这直接影响了室外计算机视觉系统的性能。本文提出了一种基于线性变换的单图像去雾快速算法,该方法假设模糊图像和无雾图像之间的最小通道之间存在线性关系。首先,分析线性变换的原理。因此,详细描述了估计媒体传输图的方法,并引入了弱化策略以解决失真最亮区域的问题。为了准确估计大气光,提出了一种基于四叉树细分的附加信道方法。在该方法中,该区域中的平均灰度和渐变被用作评估标准。最后,使用大气散射模型获得无雾图像。许多实验结果表明,该算法可以清晰自然地恢复图像,尤其是在景深突然变化的边缘。因此,它对于单图像去雾可以获得良好的效果。此外,算法时间复杂度是图像大小的线性函数。通过保证运行速度和处理效果之间的平衡,在运行时间上具有明显的优势。

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