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Feature Forwarding for Efficient Single Image Dehazing

机译：高效单像脱水的功能转发

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Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore the dependency of dehazed image quality on parameter count and model design. The first two variants presented, a small and big version, make use of a single efficient encoder–decoder convolutional feature extractor. The final variant utilizes a pair of encoder–decoders for atmospheric light and transmission map estimation. Each variant ends with an image refinement pyramid pooling network to form the final dehazed image. For the big variant of the single-encoder network, we demonstrate state-of-the-art performance on the NYU Depth dataset. For the small variant, we maintain competitive performance on the super-resolution O/I-HAZE datasets without the need for image cropping. Finally, we examine some challenges presented by the Dense-Haze dataset when leveraging CNN architectures for dehazing of dense haze imagery and examine the impact of loss function selection on image quality. Benchmarks are included to show the feasibility of introducing this approach into real-time systems.
机译：雾度变差的图像内容和信息模糊，它可以影响负面基于视觉的决策实时系统。在本文中，我们提出了一种旨在在边缘图形处理单元（GPU）上运行的高效完全卷积神经网络（CNN）图像脱水方法。我们利用我们架构的三种变体来探索去除图像质量对参数计数和模型设计的依赖性。呈现的前两个变体，小型和大版本，利用单一高效的编码器解码器卷积器特征提取器。最终变型利用一对用于大气光和传输地图估计的编码器解码器。每个变体以图像细化金字塔池汇集网络结尾，以形成最终的去除湿图像。对于单编码器网络的大变量，我们在NYU深度数据集上展示了最先进的性能。对于小型变体，我们在超级分辨率O / I-Haze数据集中保持竞争性能，而无需图像裁剪。最后，我们在利用CNN架构时检查了密集雾霾数据集的一些挑战，以便致密雾地图像去吸收，并检查损失功能选择对图像质量的影响。包括基准以显示将这种方法引入实时系统的可行性。

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