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Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample
The separation of two closely located defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows an improved reconstruction of closely located defects. This new technique has also been studied using 1D laser arrays in active thermography.
The post-processing can be roughly described by two steps: 1. Finding a sparse basis representation using a reconstruction algorithm such as the Fourier transform, 2. Application of an iterative joint sparsity (IJOSP) method to the firstly reconstructed data. For this reason, different methods in post-processing can be compared using the same measured data set.
The focus in this work was the variation of reconstruction algorithms in step 1 and its influence on the results from step 2. More precise, the measured thermal waves can be transformed to virtual (ultrasound) waves that can be processed by applying ultrasound reconstruction algorithms and finally the super resolution algorithm. Otherwise, it is also possible to make use of a Fourier transform with a subsequent super resolution routine. These super resolution thermographic image reconstruction techniques in post-processing are discussed and evaluated regarding performance, accuracy and repeatability.
Using an infrared camera for radiometric imaging allows the contactless temperature measurement of multiple surface pixels simultaneously. From the measured surface data, a sub-surface structure, embedded inside a sample or tissue, can be reconstructed and imaged when heated by an excitation light pulse. The main drawback in radiometric imaging is the degradation of the spatial resolution with increasing depth, which results in blurred images for deeper lying structures. We circumvent this degradation with blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The ground-breaking concept of super-resolution can be transferred from optics to thermographic imaging.
Photothermal radiometry with an infrared camera allows the contactless temperature measurement of multiple surface pixels simultaneously. A short light pulse heats the sample. The heat propagates through the sample by diffusion and the corresponding temperature evolution is measured at the sample’s surface by an infrared camera. The main drawback in radiometric imaging is the loss of the spatial resolution with increasing depth due to heat diffusion, which results in blurred images for deeper lying structures. We circumvent this information loss due to the diffusion process by using blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The structured illumination is realized by parallel laser lines from a vertical-cavity surface-emitting laser (VCSEL) array controlled by a random binary pattern generator. By using 150 different patterns of structured illumination and our iterative joint sparsity algorithm, it was possible to resolve 1 mm thick lines at a distance down to 0.5 mm, which results in a resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenous illuminated thermographic reconstruction.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
The work to be presented focuses on our most recent studies to laser excited super resolution (SR) thermography. The goal of nondestructive testing with SR is to facilitate the separation of closely spaced defects. Photothermal SR can be realized by performing structured illumination measurements in com-bination with the use of deconvolution algorithms in post-processing. We explain that stepwise as well as continuous scanning techniques are applicable to generate structured illumination measurements. Finally, we discuss the effect of experimental parameters and image processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.