论文标题
快速深度多斑点分层网络，用于非均匀图像
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
论文作者
论文摘要
最近，基于CNN的端到端深度学习方法在图像飞机方面具有优势，但它们在非同质性飞行中往往会巨大失败。除此之外，现有的流行多尺度方法是运行时密集型且内存效率低下。在这种情况下，我们提出了一个快速的深度多绘制层次网络，以通过从危险图像的不同空间片段中汇总来自多个图像贴片的特征，以减少网络参数的数量，以恢复非均匀的危险图像。我们提出的方法对于现场具有不同密度的不同密度的不同环境非常健壮，并且由于模型的总尺寸约为21.7 MB，因此非常轻巧。与当前的多尺度方法相比，它还提供更快的运行时，其平均运行时为0.0145s来处理1200x1600 HD质量图像。最后，我们展示了该网络在密集的雾度去除上与其他最先进模型的优越性。
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.
