Abstract
The diffusive nature of heat propagation complicates the separation of two closely spaced defects. This results in a fundamental limitation in spatial resolution. Therefore, super resolution (SR) image reconstruction can be used. SR processing techniques based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern. This paper presents the results after applying SR algorithms such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Two different data processing strategies are evaluated and discussed regarding their influence on the reconstruction goodness as well as their complexity. Moreover, the degradation of the SR reconstruction by the choice of regularization parameters in data processing is discussed The application of both SR techniques that are evaluated in this paper results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects. The fundamental difference between both SR techniques is their complexity.
How to Cite:
Ahmadi, S. ., Thiel, E. ., Karagianni, C. ., Hirsch, P. ., Ziegler, M. ., Burgholzer, P. ., Mayr, G. ., Jung, P. . & Caire, G. ., (2019) “Photothermal super resolution imaging”, Review of Progress in Quantitative Nondestructive Evaluation .
Downloads:
Download PDF