亚像素渲染
卷积神经网络
计算机科学
流离失所(心理学)
人工智能
斑点图案
数字图像相关
计算机视觉
图像处理
稳健性(进化)
位移场
数字图像处理
算法
像素
光学
图像(数学)
有限元法
物理
基因
热力学
生物化学
心理学
化学
心理治疗师
作者
Chaochen Ma,Qing Ren,Jun Zhao
出处
期刊:Optics Express
[The Optical Society]
日期:2021-03-09
卷期号:29 (6): 9137-9137
被引量:11
摘要
The subpixel displacement estimation is an important step to calculation of the displacement between two digital images in optics and image processing. Digital image correlation (DIC) is an effective method for measuring displacement due to its high accuracy. Various DIC algorithms to compare images and to obtain displacement have been implemented. However, there are some drawbacks to DIC. It can be computationally expensive when processing a sequence of continuously deformed images. To simplify the subpixel displacement estimation and to explore a different measurement scheme, a convolutional neural network with a transfer learning based subpixel displacement measurement method (CNN-SDM) is proposed in this paper. The basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by CNN, and thereby to achieve a coarse-to-fine subpixel displacement estimation. The proposed CNN is a classification model consisting of two convolutional neural networks in series. The results of simulated and real experiments are shown that the proposed CNN-SDM method is feasibly effective for subpixel displacement measurement due its high efficiency, robustness, simple structure and few parameters.
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