Spatial resolution improvement by a super-resolution technique depending on training process in the background-orientated schlieren analyses

被引:4
|
作者
Ota, Katsunari [1 ]
Ukai, Takahiro [1 ]
Wakai, Tatsuya [1 ]
机构
[1] Osaka Inst Technol, Dept Mech Engn, Osaka 5358585, Japan
基金
日本学术振兴会;
关键词
IMAGE SUPERRESOLUTION; VELOCIMETRY; PIV;
D O I
10.1063/5.0174753
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
The effectiveness of the enhanced deep super-resolution network (EDSR), which is a super-resolution technique based on the deep convolutional neural network, is investigated for the improvement of spatial resolution during a background-orientated schlieren (BOS) analysis, and its suitable training process in the EDSR method is clarified. Consequently, a training dataset consisting of the simple dot patterns leads to a better image quality due to super-resolution because the image captured in the BOS measurement shows the similar dot patterns. When the image is enlarged at a large magnification ratio, it is important to adjust an image size in the training dataset individually for each magnification ratio, thereby obtaining a good estimation accuracy of the pixel displacement in the BOS analysis. A measurement error is improved by 62% compared with that of the Bicubic method, which is a classical spatial resolution improvement technique at the magnification ratio of 8. The present result shows that the EDSR method with the best training conditions provides a reasonable pixel displacement vector field up to the magnification ratio of 8 for the BOS analysis; however, its effectiveness depends on flow structure.
引用
收藏
页数:11
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