Deep learning-based super-resolution images for synchronous measurement of temperature and deformation at elevated temperature

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Measuring temperature and deformation at elevated temperature has recently been a major concern for the engineering tests and the quality improvement of captured images is critical to it. Here, we propose a simple, high-precision and easy-to-implement technique combing the image capturing and processing methods to obtain the high-resolution images as well as the corresponding temperature and deformation fields. The super-resolution convolutional neural network (SRCNN) algorithm is used for the super-resolution reconstruction, while the deformation field is calculated by the DIC method and the temperature field is synchronous obtained by the improved two-color method. Flame heating experiment of the SiC material validated the applicability and the improvement of the proposed method, showing the improvement in image quality (with PSNR increased 16.7 %) and calculation accuracy of temperature and deformation (with error decreased 3.57 %). Meanwhile, the phenomenon of "feature point drift" is pointed out and the error of deformation measurement for high-resolution and low-resolution is analyzed. Furthermore, the sub-pixel edge detection can be achieved by the combination of the proposed method and the edge detection method, which shows a potential value in detecting of surface defects or cracks.