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Densely pyramidal residual network for UAV-based railway images dehazing

机译：基于无人机的铁路图像去雾的密集金字塔残差网络

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摘要

On purpose of aiding detection and recognition for railway infrastructure and dramatic changes in the environment around railways, visual inspection based on unmanned aerial vehicle (UAV) images is a highlight. However, UAV images often suffer from degradation for fog or haze, which limits the inspection efficiency. Most existing methods depend on a suboptimal two-step network with much more redundant procedures where transmission map and atmospheric light are estimated at first, and then haze-free images can be acquired using a dehazing model. This paper presents a novel end-to-end network for UAV-based railway images dehazing, and focuses on two key issues: network architecture and loss function. With regards to the first aspect, based on a pyramidal network structure, densely pyramidal residual network (DPRnet) consists of dense residual block and enhanced residual blocks, which heavily exploits the feature maps of all preceding layers and considerably increased depth at different scale, respectively. With regards to the second, a new loss function introducing structural similarity index is proposed to preserve more structural information, thereby restore the appealing perceptual quality of the hazy images. Finally, quantitative and qualitative evaluations illustrate that the DPRnet achieves better performance over the classic methods, yet remains efficient and convenient. (C) 2019 Published by Elsevier B.V.
机译：为了帮助检测和识别铁路基础设施以及铁路周围环境的急剧变化，基于无人飞行器（UAV）图像的目视检查是一大亮点。但是，UAV图像通常会因雾气或霾而退化，这限制了检查效率。多数现有方法依赖于次优两步式网络，该网络具有更多冗余过程，其中首先估算透射图和大气光，然后可以使用除雾模型获取无雾图像。本文提出了一种用于基于无人机的铁路图像去雾的新型端到端网络，并着重于两个关键问题：网络架构和损失功能。关于第一方面，基于金字塔网络结构，密集金字塔残差网络（DPRnet）由密集残差块和增强残差块组成，它们分别大量利用了所有先前层的特征图和在不同尺度上显着增加的深度。关于第二种，提出了一种新的引入结构相似性指标的损失函数，以保留更多的结构信息，从而恢复朦胧图像的吸引人的感知质量。最后，定量和定性评估表明，DPRnet与传统方法相比具有更好的性能，但仍然高效且方便。 （C）2019由Elsevier B.V.发布

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