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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems
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An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems

机译：智能交通系统捕捉道路场景的高效能见度增强算法

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摘要

The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. In sandstorms, atmospheric sand has a propensity to irregularly absorb specific portions of a spectrum, thereby causing color-shift problems in the captured image. Traditional state-of-the-art restoration techniques are unable to effectively cope with these hazy road images that feature localized light sources or color-shift problems. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The performance of the proposed method has been proven through quantitative and qualitative evaluations. Experimental results demonstrate that the proposed haze removal technique can more effectively recover scene radiance while demanding fewer computational costs than traditional state-of-the-art haze removal techniques.
机译：在恶劣天气条件下拍摄时，室外道路场景图像的可见性通常会下降。驾驶员经常打开车辆的前灯，并且经常激活路灯，从而在这些条件下在捕获道路场景的图像中产生局部光源。此外，沙尘暴也是某些地区开车时经常遇到的天气事件。在沙尘暴中，大气沙具有不规则地吸收光谱的特定部分的倾向，从而在捕获的图像中引起色移问题。传统的最新修复技术无法有效地解决这些模糊的道路图像，这些图像具有局部光源或色偏问题。作为响应，我们提出了一种新颖且有效的除雾方法，以解决由局部光源和色偏引起的问题，从而为单个模糊图像实现出色的还原效果。通过定量和定性评估证明了该方法的性能。实验结果表明，与传统的除雾技术相比，该除雾技术可以更有效地恢复场景辐射，同时所需的计算成本更低。

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