论文标题
新颖且有效的基于CNN的二进化，用于历史上降级的绘图图
Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps
论文作者
论文摘要
历史上的二进制化降级效果绘图（HDAD）地图是一项新的挑战性工作，尤其是在删除三个工件，即噪声，黄色区域和折叠线的方面，同时可以很好地保留前景组件。在本文中，我们首先提出了一种半自动标记方法，以创建每个HDAD PAIR的HDAD PAIR数据集，该数据集由一个HDAD PAIR组成，由一个HDAD MAP及其二进制HDAD MAP组成。基于创建的HDAD PAIR数据集，我们提出了一种基于卷积神经网络（基于CNN的）二进制方法，以生成高质量的二氧化HDAD地图。基于测试HDAD图，彻底的实验数据表明，就精度，PSNR（峰值信号到噪声）而言）以及二进制HDAD图的感知效应，我们的方法显着优于九种现有的二进制方法。此外，具有相似的精度，实验结果证明了我们方法相对于基于最先进的CNN的二进制方法的重新训练版本的执行时间降低优点。
Binarizing historically degraded as-built drawing (HDAD) maps is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a semi-automatic labeling method to create the HDAD-pair dataset of which each HDAD-pair consists of one HDAD map and its binarized HDAD map. Based on the created training HDAD-pair dataset, we propose a convolutional neural network-based (CNN-based) binarization method to produce high-quality binarized HDAD maps. Based on the testing HDAD maps, the thorough experimental data demonstrated that in terms of the accuracy, PSNR (peak-signal-to-noise-ratio), and the perceptual effect of the binarized HDAD maps, our method substantially outperforms the nine existing binarization methods. In addition, with similar accuracy, the experimental results demonstrated the significant execution-time reduction merit of our method relative to the retrained version of the state-of-the-art CNN-based binarization methods.
