计算机科学
计算机视觉
人工智能
图像增强
图像处理
迭代重建
光学
图像(数学)
物理
摘要
Active thermography has gained broad acceptance as a non- destructive evaluation method for numerous in-service and manufactur- ing applications in the aerospace industry. However, because of the dif- fusive nature of the process, it is subject to blurring and degradation of the signal as one attempts to image deeper subsurface features. Despite this constraint, active thermographic response is deterministic, to the extent that the postexcitation time evolution for a defect-free sample can be accurately predicted using a simple one-dimensional model. In the patented thermal signal reconstruction method, the time history of every pixel in the field of view is compared to such a model in the logarithmic domain, where deviations from ideal behavior are readily identifiable. The process separates temporal and spatial nonuniformity noise compo- nents in the image sequence and significantly reduces temporal noise. Time-derivative images derived from the reconstructed data allow detec- tion of subsurface defects at earlier times in the sequence than conven- tional contrast images, significantly reducing undesirable blurring effects and facilitating detection of low-thermal-contrast features that may not be detectable in the original data sequence. © 2003 Society of Photo-Optical In-
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