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

Abstract

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.