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Effective visibility restoration and enhancement of air polluted images with high information fidelity

机译:有效地恢复可见性并以高信息保真度增强空气污染图像

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In this paper, an effective approach for dehazing and enhancing outdoor images is proposed. The dark channel prior is used to estimate a raw transmission map. This transmission map is then refined using guided image filtering under the guidance of the hazy image. For colors and details enhancement, the adaptive manifolds high-dimensional filtering is applied to the recovered scene radiance. The proposed approach is compared with other enhancement techniques and effective quality assessment methods are used for objective evaluation. The visual information fidelity (VIF) metric is used to quantify the loss of image information, since contrast overcompensation may cause information loss. The structural similarity index (SSIM) is used to quantify the degradation of structural information between the enhanced and recovered image. Other metrics including the ratio between the gradient of visible edges before and after enhancement are also computed. Experimental results show that combining guided image filtering for refining the raw transmission map, with adaptive manifolds filtering for image enhancement, produces images with improved colors, high contrast and minimum information loss.
机译:本文提出了一种对室外图像进行除雾和增强的有效方法。暗信道先验用于估计原始传输图。然后在朦胧图像的引导下使用引导图像过滤对该传输图进行细化。为了增强色彩和细节,将自适应流形高维滤波应用于恢复的场景辐射度。将所提出的方法与其他增强技术进行比较,并使用有效的质量评估方法进行客观评估。视觉信息保真度(VIF)度量用于量化图像信息的丢失,因为对比度过度补偿可能会导致信息丢失。结构相似性指数(SSIM)用于量化增强图像和恢复图像之间结构信息的退化。还计算其他度量,包括增强前后可见边缘的梯度之间的比率。实验结果表明,结合使用引导图像过滤来细化原始透射图,以及使用自适应流形过滤来增强图像,可以生成具有改进的色彩,高对比度和最小信息损失的图像。

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