Vision systems are the core information collection module in outdoor industrial systems such as factory inspection robots. However, haze greatly reduces working efficiency. Existing dehazing methods have two problems—first, they are not specifically designed for the industrial systems; second, these methods include several assumptions in their design processes and imaging models, leading to unsatisfactory results. In this article, an approach for single image dehazing is proposed to improve the efficiency of outdoor vision-based systems. First, a novel haze imaging model is proposed based on the dichromatic atmospheric scattering model. It considers the effects of multiple scattering and involves fewer assumptions. Then a data-driven technique called sparse representation is used to solve this model. Considering a haze image, a distorted and blurred version of a fine image, every patch is presented using dedicatedly prepared over-complete dictionaries and is traced back to a haze-free image. Quantitative and qualitative comparisons on a number of real-world haze images demonstrate that the proposed approach not only is more stable but also leads to better dehazing results.

中文翻译：

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有效的数据驱动技术，用于高效的基于视觉的户外工业系统

视觉系统是户外工业系统（例如工厂检查机器人）中的核心信息收集模块。但是，雾度大大降低了工作效率。现有的除雾方法有两个问题-首先，它们不是专门为工业系统设计的；其次，这些方法在其设计过程和成像模型中包含多个假设，导致结果不理想。在本文中，提出了一种用于单图像去雾的方法，以提高基于室外视觉的系统的效率。首先，基于双色大气散射模型，提出了一种新颖的雾度成像模型。它考虑了多重散射的影响，并且涉及较少的假设。然后使用一种称为稀疏表示的数据驱动技术来解决该模型。考虑到雾度图像，作为精细图像的扭曲和模糊版本，每个补丁都使用专门准备的过度完整字典进行呈现，并追溯到无雾图像。在大量真实雾度图像上的定量和定性比较表明，所提出的方法不仅更稳定，而且导致更好的除雾效果。