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Effective Data-Driven Technology for Efficient Vision-Based Outdoor Industrial Systems

机译：基于高效视觉户外工业系统的有效数据驱动技术

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摘要

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.
机译：视觉系统是户外工业系统中的核心信息收集模块，如工厂检测机器人。然而，雾度极大地降低了工作效率。现有的脱水方法有两个问题 - 首先，它们不是专为工业系统设计的;其次，这些方法包括其设计过程和成像模型中的几种假设，导致结果不令人满意。在本文中，提出了一种用于单幅图像脱落的方法，以提高基于视觉的系统的效率。首先，提出了一种基于二色大气散射模型的新型雾霾成像模型。它考虑了多次散射的影响，并且涉及更少的假设。然后使用称为稀疏表示的数据驱动技术来解决此模型。考虑到阴霾图像，扭曲和模糊的细图像版本，使用专用的完整的字典来呈现每个补丁，并追溯到无阴霾图像。关于许多现实世界的阴霾图像的定量和定性比较表明，所提出的方法不仅更稳定，而且导致更好的脱水结果。

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