Optimal Transmission Estimation via Fog Density Perception for Efficient Single Image Defogging
Optimal Transmission Estimation via Fog Density Perception for Efficient Single Image Defogging
复制标题
通过雾密度感知进行最佳传输估计，以实现高效的单图像去雾
DOI：
10.1109/tmm.2017.2778565
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发表时间：
2018-07-01
影响因子：
7.3
通讯作者：
Lu, Xiao
中科院分区：
文献类型：
--
作者：
Ling, Zhigang;Gong, Jianwei;Lu, Xiao
基于先验假设或约束的单幅图像去雾算法因其简单性和实用性而备受关注。然而，在处理天气条件下拍摄的雾状图像方面，他们仍然面临一些挑战，在这种情况下，这些假设或约束可能不够有效或不够有效。本文的目的是提出一种新的图像去雾算法，通过直接预测恢复图像的雾密度，而不是采用先验假设或约束。为了实现这一目标，引入了两个具体步骤。首先，我们采用了从雾图像中提取的三个与雾相关的统计特征，并通过创建这些与雾相关的特征的线性组合来进一步开发简单的雾密度估计器(SFDE)。与已有的方法相比，该方法可以有效地感知单幅图像的雾密度，而不需要参考相应的无雾图像，并且具有较低的计算量。其次，通过SFDE建立了图像透过率与雾度分数之间的物理数学关系，从而将图像去雾归结为对恢复图像雾度分数的最小化问题。因此，提出了两种最优传输模型，称为基于SFDE的最优传输模型(OTSFDE)和更简单的基于SFDE的最优传输模型(SOTSFDE)，以确定有效除雾的关键传输映射。与OTSFDE相比，SOTSFDE的计算复杂度较低，性能略有下降。实验结果表明，与已有的一些算法相比，该算法能够有效地去除雾，并且在定量和定性上都不受任何假设或约束的限制。
Single image defogging algorithms based on prior assumptions or constraints have captured much attention because of their simplicity and practicality. However, they still have some challenges to deal with foggy images captured under weather conditions where these assumptions or constraints may not be effective or efficient enough. In this paper, we aim to develop a novel image defogging algorithm by directly predicting the fog density of recovered images rather than adopting prior assumptions or constraints. In order to achieve this goal, two specific steps are introduced. First, we adopt three fog-relevant statistical features derived from foggy images, and further develop a simple fog density evaluator (SFDE) by creating a linear combination of these fog-relevant features. This proposed evaluator can efficiently perceive the fog density of a single image without reference to a corresponding fog-free image and has a low computational load compared with an existing method. Second, a physics-based mathematical relationship between the transmission and the fog density score of the recovered image is developed via SFDE, thus image defogging can be posed as a minimization problem on the fog density score of the recovered image. As a result, two optimal transmission models, called an optimal transmission model via SFDE (OTSFDE) and a simpler optimal transmission models via SFDE (SOTSFDE), are present to determine the key transmission map for efficient fog removal. Compared to OTSFDE, SOTSFDE has low computational complexity with slight performance degradation. Experimental results demonstrate that the proposed algorithms can effectively remove fog and are not confined by any assumptions or constraints, both quantitatively and qualitatively, compared with some existing algorithms.