In order to improve the precision and efficiency of precision equipment assembly, a high-precision industrial measurement system is construction by using total station and Spatial Analyzer software. Based on the basic principle of angle forward intersection, an industrial measurement system is designed with two total stations, a scale bar, a link, several data cables and a computer. And the online control and data processing are realized with Spatial Analyzer. The test results indicate that the measurement accuracy of this system can reach 0.1mm, and it can also evaluate and analyse the measurement results, providing a feasible solution for high-precision assembly of industrial equipment.
The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)- based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low- and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.
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