Wu Yinan, Fang Yongchun, Wang Chao, Liu Cunhuan, Fan Zhi
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin, 300350, China.
Institute of Robotics and Automatic Information System, Nankai University, Tianjin, 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin, 300350, China.
Ultramicroscopy. 2020 Jun;213:112991. doi: 10.1016/j.ultramic.2020.112991. Epub 2020 Apr 6.
An atomic force microscopy generally adopts a raster scanning method to obtain the image of the sample morphology. However, the raster method takes too much time on the base part without focusing enough on the object, thereby restricting the scanning speed of an AFM. To solve this problem, this paper proposes a novel path planning based scanning method to achieve high-speed scanning with super resolution for AFMs. Specifically speaking, a fast scanning process is first carried out to generate a low-resolution image with less time, then a convolutional neural network is designed to construct a super-resolution image based on the fast scanning image. Afterwards, an advanced detection algorithm is proposed to achieve the accurate object detection and localization. Furthermore, an improved ant colony optimization algorithm is proposed to realize the path planning for scanning the objects with high quality, whose imaging result is then matched with the previous super-resolution image to construct the entire sample image, thus achieving fast scanning with super resolution. Experimental and application results demonstrate the good performance of the proposed scanning method.
原子力显微镜通常采用光栅扫描方法来获取样品形态的图像。然而，光栅方法在基础部分花费的时间过多，而对目标的聚焦不足，从而限制了原子力显微镜的扫描速度。为了解决这个问题，本文提出了一种基于路径规划的新型扫描方法，以实现原子力显微镜的超分辨率高速扫描。具体而言，首先进行快速扫描过程以在较短时间内生成低分辨率图像，然后设计一个卷积神经网络基于快速扫描图像构建超分辨率图像。之后，提出一种先进的检测算法以实现准确的目标检测和定位。此外，提出一种改进的蚁群优化算法以实现对物体进行高质量扫描的路径规划，其成像结果随后与先前的超分辨率图像进行匹配以构建整个样品图像，从而实现超分辨率的快速扫描。实验和应用结果证明了所提出扫描方法的良好性能。
