Blind structured illumination as excitation for super-resolution photothermal radiometry

Abstract

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 groundbreaking concept of super-resolution can be transferred from optics to thermographic imaging.

Super resolution laser thermography

“…It can be clearly seen that the application of a compressed sensing based algorithm such as the IJOSP approach, which uses the block-FISTA for optimization, highly improves the detection sensitivity and delivers clear peaks, although sometimes associated with uncertainties regarding the position. This interesting result could already be seen in [1,5] using high-power VCSEL arrays in a steady state mode. Our studies show that this super resolution technique can also be applied on thermographic data from laser line scanning which is of high interest for industrial applications and therefore provides a solution to enhance the spatial resolution.…”

“…Sparsity enters the equation in form of a L1 regularization term. The iterative joint sparsity approach IJOSP [1,4] is also minimizing this difference but considers the joint sparsity for all measurements, which means it only leads to a nonzero entry in the solution if several measurements show a signal at the same position. These positions are then identified as the defect positions.…”

“…This can be circumvented by using super resolution techniques such as the blind structured illumination in combination with an approach from mathematical optimization theory and compressed sensing. Such compressed sensing algorithms are useful within sparse systems since they make use of joint sparsity [1]. In our thermal images the sparsity is given by the small number of defects or more precisely by the small relation between number of pixels which are in the defect area and number of pixels in the non-defect area.…”

Extension of the Thermographic Signal Reconstruction Technique for an Automated Segmentation and Depth Estimation of Subsurface Defects

“…On the other hand, more advanced methods such as the VWC can be used to produce detailed defect maps. In addition, VWC can deal with structured excitation [ 22 ], enabling defect resolutions which cannot be achieved with PT. Algorithms such as PPT and TSR have short computing times, but have difficulties with more complex sample geometries, such as varying sample thicknesses.…”

Laser excited super resolution thermal imaging for nondestructive inspection of internal defects

“…Novel photothermal SR approaches combine the SI measurement strategy and CS based processing algorithms, such as the iterative joint sparsity algorithm (IJOSP) to better separate two closely spaced defects 7,[23][24][25] . So far, the suitability of these techniques has been investigated in transmission configuration with anomalies on the backside of the specimen.…”