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    • 专利摘要:
    The invention relates to a method for recording thermal images of an imaging structure (S) arranged below a sample surface (P) with a thermal imaging camera (K) receiving the sample surface (P), a source (Q) of electromagnetic radiation for illuminating the structure (S) to be imaged with an evaluation unit (A) for evaluating the recorded by the thermal imaging camera (K) Oberflächenmeßdaten. In order to improve the depth resolution, it is proposed that the structure (S) to be imaged be illuminated with an unknown structured illumination and thus heated, wherein several images are used to evaluate the structure (S) and the structure (S) per image otherwise structured illumination is irradiated and that for calculating the structure to be imaged (S) from the images recorded with the thermal imager (K) a non-linear iterative evaluation algorithm is used, the thin occupation and the constant location of the heated structure for the differently structured illumination patterns exploits.
    公开号:AT520007A1
    申请号:T50421/2017
    申请日:2017-05-16
    公开日:2018-12-15
    发明作者:Peter Burgholzer Dr
    申请人:Res Center For Non Destructive Testing Gmbh；
    IPC主号:
    专利说明:

    Summary
    The invention relates to a method for recording thermal images of a structure (S) arranged under a sample surface (P) with a thermal imaging camera (K) recording the sample surface (P), a source (Q) of electromagnetic radiation for illuminating the structure to be imaged (S) and proposed with an evaluation unit (A) for evaluating the surface measurement data recorded by the thermal imaging camera (K). To improve the depth resolution, it is proposed that the structure (S) to be imaged be illuminated with an unknown structured illumination and thus heated for improved reconstruction, several images being used for evaluating the structure (S) and the structure (S) with one for each image differently structured lighting is illuminated and that to calculate the structure to be imaged (S) from the images taken with the thermal imager (K), a non-linear iterative evaluation algorithm is used, which uses the thin population and the constant location of the heated structure for the differently structured lighting patterns exploits.
    (Fig. 3) / 26 (41476) HEL
    The invention relates to a method and a device for recording thermal images of a structure to be imaged arranged under a sample surface, with a thermal imaging camera receiving the sample surface, a source of electromagnetic radiation for illuminating the structure to be imaged and with an evaluation unit for evaluating the surface measurement data recorded by the thermal imaging camera.
    The use of an infrared camera for the recording of thermal images enables the contactless and simultaneous temperature measurement of many surface pixels. From this surface measurement data, a structure embedded in a sample, a tissue or the like below a surface can be reconstructed and displayed if it is heated by an excitation pulse. The main disadvantage in active thermography imaging is the loss of spatial resolution proportional to the depth below the sample surface. This results in blurry images for deeper structures.
    For many imaging methods, the possible spatial resolution is limited by the width of the point spread function (PSF), namely the image of a small object, ideally a point. In acoustics, this corresponds to the diffraction limit or, in terms of appearance, to the Abbe limit. Both limits are proportional to the acoustic or optical wavelength. For smaller structures, either higher spatial frequencies corresponding to shorter wavelengths, e.g. of electrons, or near-field effects. This is often not possible for biomedical and non-destructive imaging because the structures are embedded in a sample or in a tissue. Therefore, they are for / 26
    Near field methods not accessible. Higher frequencies are attenuated below the noise level before they can be detected on the surface. Other high-resolution methods are required to display such structures.
    In their theory of high resolution, Donoho et al. (D.L. Donoho, A.M. Johnstone, J.C. Hoche, and A.S. Stern, J.R. Statist. Soc. B 54, 41 (1992)) showed that high-resolution imaging can overcome such a resolution limit. When the noise goes to zero, the reconstructed image converges to the original object. For diffraction limited imaging, they showed that nonlinear algorithms that obey a positivity limitation can get high resolution. As early as 1972, Frieden (BR Frieden, J. Opt. Soc. Am. 62, 1202 (1972)) showed for a simulated object consisting of two narrow lines that could not be resolved with a regression calculation based on the principle of least squares its non-linear reconstruction algorithm can resolve and display the object.
    In 1999, five years after their theoretical description, the first high-resolution far-field fluorescence microscopy was realized experimentally with STED microscopy (T. A. Klar and S. W. Hell, Opt. Lett. 24, 954 (1999)). Other high-resolution methods such as STORM, PALM or SOFI later emerged, all of which take advantage of the fact that localization of point sources (e.g. activated fluorescent molecules) is possible with greater accuracy than the width of the PSF.
    Structured illumination microscopy (SIM - M. G. Gustafsson, J. Microscopy 198, 82 (2000)) uses several structured patterns as illumination for high-resolution imaging. The physical origin of the increase in resolution is a frequency mix between frequencies of the lighting and the object frequencies. The high spatial frequencies in the object are transformed by this frequency mixing into the low-frequency range by the Fourier transform of the PSF and can therefore be mapped. Normally, reconstruction / 26 algorithms use knowledge of the lighting pattern of the structured lighting for the calculation of the images. However, even small errors in the patterns can lead to errors in the final images. A blind SIM was therefore proposed, in which knowledge of the lighting pattern is not necessary. The lighting patterns are believed to be positive and their sum is homogeneous (E. Mudry, K. Belkebir, J. Girard, J. Savatier, EL Moal, C. Nicoletti, M. Allain, and A. Sentenac, Nat. Photon. 6, 312 (2012)), or additional restrictions such as the same absorption patterns for all lighting, thin occupation of the functions or requirements for the covariance of the patterns can be applied. Recently, two reconstruction algorithms have been proposed that use sparse population and equality of absorption patterns (so-called block sparsity) that have been successfully used for the acoustic resolution of photoacoustic microscopy. The spatial resolution limit given by the acoustic PSF could thus be largely improved by using lighting with unknown granular laser patterns ("speckle pattern"). The reconstruction algorithms used are also valuable for other imaging methods in which diffuse processes confuse high-frequency structural information.
    Thermographic imaging uses the pure diffusion of heat, sometimes referred to as thermal waves, whereby the structural information of thermal images is attenuated much more at higher image depths than by acoustic attenuation. Thermographic imaging has several advantages over other imaging techniques, e.g. ultrasound imaging. No coupling media such as water are required, and the temperature development of many surface pixels can be measured in parallel and without contact using an infrared camera. The main disadvantage of thermographic imaging is the sharp decrease in spatial resolution proportional to depth, which leads to blurry images for deeper structures.
    The object of the invention is to provide a method and an associated device for recording thermal images which, compared to the prior art ei / 26 ne, enable markedly improved depth resolution with thermal images of measured structures. In particular, structures that are deeper below a surface should also be shown in an improved manner.
    The invention solves this problem with the features of independent claim 1. Advantageous developments of the invention are presented in the subclaims.
    The invention overcomes the disadvantage, namely the loss of the spatial resolution proportional to the depth below the sample surface and also enables higher resolution for deeper structures by using (unknown) structured illumination and by using a non-linear iterative evaluation algorithm, who uses the sparsity and the constant location of the heated structures for the different structured lighting patterns (IJOSP algorithm - TW Murray, M. Haltmeier, T. Berer, E. Leiss-Holzinger, and P. Burgholzer , Optica 4, 17 (2017).
    The unknown structured lighting can be light that falls through moving slit diaphragms, as shown below in one embodiment. When using coherent light (laser, microwave or the like), in a scattering sample, such as a biological tissue, dark and light spots, called laser speckles, automatically appear due to interference phenomena, which means that a separate aperture can be omitted if necessary. These speckle patterns are used as unknown structured lighting and the size of the bright areas (speckles) depends on the light wavelength of the laser, the scattering properties of the sample and the depth of penetration of the light in the sample.
    According to the invention, the effect of the resolution, which decreases proportionally with the depth, can be avoided if a known or also unknown structured illumination and a non-linear reconstruction algorithm are used to reconstruct the embedded structure. This means, for example, that line patterns or star-shaped structures are represented by a 3 mm thick / 26
    Sheet steel possible with a resolution that is at least significantly better than the width of the thermographic point response (“point spread function, PSF for short). Further details are shown in the exemplary embodiment.
    In order to avoid the disadvantage of the sharp decrease in the spatial resolution proportional to the depth of a sample under the sample surface, an unknown structured illumination is used according to the invention together with an iterative algorithm which uses the thin population of the structures. The reason for this decrease in resolution with increasing depth is the entropy production during the diffusion of heat, which for macroscopic samples is equal to the loss of information and therefore limits the spatial resolution. The mechanism for loss of information is thermodynamic fluctuation, which is extremely small for macroscopic samples. However, these fluctuations are highly amplified when reconstructing structural information from thermographic data (“badly posed” inverse problem). Entropy production, which only depends on the mean temperature values, is the same for macroscopic samples as the loss of information caused by these fluctuations. For real heat diffusion processes, these fluctuations cannot be described by simple stochastic processes, but with macroscopic samples, the loss of information depends only on the amplitude of the fluctuations in relation to the mean temperature signals, which corresponds to the signal-to-noise ratio (SNR). With this knowledge, it is possible to derive a PSF from the SNR without calculating the loss of information and entropy production.
    In particular, the thermographic reconstruction takes place in a three-stage process. In a first step, the measured time-dependent temperature signals TS (r, t) are converted as a function of the location r and the time t into a virtual acoustic signal (see P. Burgholzer, M. Thor, J. Gruber, and G. Mayr, J. Appl. Phys. 121, 105102 (2017)). In a second step, an ultrasound reconstruction method (e.g. FSAFT) is used to reconstruct y (r) as a spatial function. In a third step, the IJOSP algorithm, a nonlinear iterative algorithm that works only in space, is used for thermographic reconstruction (TW Murray, M. Haltmeier, T. Berer, E. Leiss-Holzinger, and P. Burgholzer, Optica 4, 17 (2017)).
    In the drawing and in the following embodiment, the invention is shown for example. Show it
    1 shows a point source, its thermographic image in Fourier space and its thermographic image in real space, FIG. 2 shows a test arrangement for linear structures to be measured, FIG. 3 shows various reconstruction examples of the linear structures, FIG .
    Fig. 5 reconstruction results for a star-shaped structure, and
    6 shows an alternative test arrangement for any three-dimensional structures to be measured in a scattering sample.
    1 (a) shows a point source at a depth d with a unit vector (ez) perpendicular to the surface plane: The length a of the thermal wave reaching the surface plane depends on the angle θ. 1 (b) shows a two-dimensional (or a cross-section of a three-dimensional) PSF in the Fourier space. Up to kcut @) (Eq. 5) the value of the PSF is one and above kcut zero. The length a becomes infinite parallel to the detection surface (θ = 90 °), which is why no thermal waves can reach the surface in this direction. Fig. 1 (c) shows the two-dimensional PSF in real space. The lateral resolution (vertical direction) is 2.44 times the axial resolution (horizontal direction). The axial resolution (horizontal arrows) for the pulsed thermography is limited by kcut and is therefore proportional to the depth d, divided by the natural logarithm of the SNR.
    2a shows a device for recording thermal images of a structure S arranged under a sample surface P with a thermal imaging camera K for recording the sample surface P, with a source Q of electromagnetic radiation for illuminating the structure S and with an evaluation unit A for evaluating the structure Surface measurement data recorded by the thermal imaging camera K, the thermal imaging camera K being directed against the sample surface P such that it records thermal images of the structure S to be imaged arranged under a sample surface P and that the source Q of electromagnetic radiation for illuminating the structure S on the opposite side of the thermal imaging camera K. Side of the sample surface P is arranged and directed against the structure S to be imaged. A diaphragm B for structured illumination of the structure S is arranged between the source Q and the structure S, the diaphragm B being displaceable relative to the structure S, in the present case being parallel to the sample surface P.
    The structure S is applied to the back of a 3 mm steel sheet. In Fig. 2 (b), four pairs of lines extending in the y direction are used as light-absorbing patterns. The distance between the lines (from left to right) is 2 mm, 1.3 mm, 0.9 mm and 0.6 mm with a line width of 1 mm. To produce structured lighting (FIG. 2 (c)), slots were cut into an aluminum foil acting as screen B at a distance of 10 mm, the slots having a width of 1 mm and running parallel to the absorption lines. Through these slots, the flash can energize the surface of the back of the steel sheet. An infrared camera (frame rate 800 Hz, 320 x 32 pixels, 6 pixels per mm) measures the surface temperature development on the front of the steel sheet. After each measurement, the slit mask is shifted in the x direction with a step size of 0.2 mm. In the exemplary embodiment, 55 measurements are used to reconstruct the positions of the absorbing line pairs from the recorded images.
    3 shows a two-dimensional reconstruction example (for the aforementioned parallel line pairs). Fig3 (a) represents an average signal TS (x, t) of all speckle patterns, which is equal to the measured signal without the slit mask. 3 (b) and (c) represent the measured surface temperature TS (x, t) for the illumination with two different speckle patterns. FIG. 3 (d) are the thermographic reconstructions ym (x) for the two different ones Illumination pattern (Fig.
    / 26
    3 (b) and (c) m = 10 (dotted) and m = 19 (dash-dotted)), as well as the reconstruction of the mean y (x) (solid) shown in FIG. 3 (a). The vertical lines between FIGS. 3 (a) to (d) show the shift of the maximum for the individual speckle patterns, which subsequently enable the high-resolution reconstruction of the line positions.
    FIG 4. Shows a mean reconstruction (bold), an R-L (Richardson-Lucy) deconvolution (dotted) and an iterative reconstruction (IJOSP, dash-dotted).
    5 shows reconstruction results using a two-dimensional star-shaped sample with 165 illumination patterns, 55 illumination patterns with slits running in the y direction and 55 illumination patterns each with slits running in the ± 45 ° direction. Fig. 5 (a) The object is a star-shaped sample consisting of 12 lines, each about 1 mm thick. The reconstructed objects were calculated in FIG. 5 (b) from the mean temperature signal, in FIG. 5 (c) with the RL (Richardson-Lucy) deconvolution and in FIG. (D) with that of the iterative reconstruction (IJOSP) , The pixel size was 0.21 mm, which resulted in 4.75 pixels per 1 mm and a total of 128 x 128 pixels. The camera refresh rate was 500 Hz.
    6a and the enlarged detail of the scattering sample thereof in FIG. 6b show a device for recording thermal images of a structure S arranged under a sample surface P with a thermal imaging camera K for recording the sample surface P, with a coherent source Q of electromagnetic radiation for illuminating the Structure S and with an evaluation unit A for evaluating the surface measurement data recorded by the thermal imaging camera K, the thermal imaging camera K being directed against the sample surface P such that it records thermal images of the structure S to be imaged arranged under a sample surface P and that the source Q generates electromagnetic radiation for Illuminating the structure S is arranged on the same side as the thermal imaging camera K with respect to the sample surface P and is directed against the structure S to be imaged. The control unit of the thermal imaging camera K and the source Q, a pulse laser or a pulsed microwave source, is indicated in the evaluation unit by two diagrams 26/26 one above the other. First, a short excitation pulse is sent, after which (if necessary also simultaneously) the thermal imager takes a sequence of images for a predetermined time interval. This process is now repeated several times, it being essential that the speckle pattern formed by interference of the coherent electromagnetic radiation changes from pulse to pulse inside the scattering sample (unknown structured illumination). In a living biological tissue this happens by slight movement by itself, for other samples (e.g. plastics) the change of the speckle pattern from one pulse to the next pulse can be caused by a slight movement of the sample or source (rotation or displacement) ,
    Embodiment:
    In order to derive the thermographic PSF, the attenuation of a one-dimensional thermal wave is first dealt with
    T (z, t) = Real {T () e ,! ' ffZ (1) where T (z, t) is the temperature as a function of the depth z of the sample and the time t, To is a complex constant to satisfy the boundary condition on the surface with z = 0, σ is the complex Wavenumber and ω = 2nf, corresponds to the thermal wave frequency.
    This solves the thermal diffusion equation
    4 £ H, Ö = 0, mi t ( 2 ) where V 2 is the Laplace operator, i.e. the second derivative in space, α is the material-dependent thermal diffusion coefficient that is assumed to be homogeneous in the sample and μ = ^ 2α / ω is defined as the thermal diffusion length, where the / 26
    The amplitude of the heat wave is reduced by a factor of 1 / e. This results in Eq. (1) as follows:
    which describes an exponentially damped wave in z with the wave number or the spatial frequency kΞ1 / μ. The cut off wave number kcut, at which the signal is attenuated to the noise level for a depth z = a, results from Eq. (3) to:
    cxp (k cu t &) k cut
    InSNR
    A spatial frequency higher than kcut cannot be resolved because the signal amplitude falls below the noise level at a distance a. The same result can be derived for one-dimensional heat diffusion by making the loss of information equal to the mean entropy production. To obtain a two or three-dimensional thermographic PSF, a point source is embedded in a homogeneous sample at a depth d with respect to a flat measuring surface. The distance a to the surface depends on the angle θ (Fig. 1 (a)):
    1 (b) shows a two-dimensional PSF or a cross section of a three-dimensional thermographic PSF in the Fourier space. In all directions up to kcut @) the value of the PSF is one and above kcut zero.
    For a chosen experimental arrangement (see Fig. 2) the depth d = 3mm (= thickness of a steel sheet) and the effective SNR = 2580. Fig. 1 (c) shows the calculated two-dimensional thermographic PSF in real space, that of the inverse Fourier -Transformation from Fig. 1 (b), calculated by the two-dimensional inverse Fourier transform. The axial depth resolution is kcut = 2.62mm -1 from Eq. (5) limited at θ = 0, which is the same as in the one-dimensional case according to Eq. (4). The zero points at a depth z = d ± n / kcut = 3 mm ± 1.2 mm are shown in Fig. 1/26 (c) by two horizontal arrows, which leads to an axial resolution of 2.4 mm. The lateral resolution (5.85 mm vertical direction in Fig. 1 (c)) is 2.44 times the axial resolution.
    The lateral resolution of this PSF is used in the following for the deconvolution or for the IJOSP reconstruction algorithm, which enables high resolution. The same PSF can be reconstructed from a point source using a two-stage image reconstruction method. First, the measured signal is converted into virtual acoustic waves (see P. Burgholzer, M. Thor, J. Gruber, and G. Mayr, J. Appl. Phys. 121, 105102 (2017)), after which every available ultrasound reconstruction method, such as the method of synthetic aperture focusing (F-SAFT) can be used for the reconstruction. This procedure only gives a sensible PSF if the measuring time is sufficient to measure the signals up to θ «45 ° and to use them for the reconstruction. With shorter measuring times, only a small cone of the PSF in the Fourier space has the value one in the axial direction and the rest has the value zero. In real space, the axial resolution remains almost constant for shorter measuring times, while the lateral resolution becomes worse.
    An experimental setup to illustrate this inventive method for high-resolution thermographic imaging includes the following. A 3 mm thick steel sheet (common structural steel with a temperature conductivity of 16mm 2 s - 1 ) was blackened on both sides for improved heat absorption and dissipation. An absorbent pattern, such as parallel lines or a star, was created on the back of the steel sheet using an aluminum foil that acts as a reflective mask. This ensures that only the unmasked (black) patterns absorb light from an optical flash assembly (PB G 6000 from Blaesing with 6 kJ electrical energy) that illuminates this side. An infrared camera (Ircam Equus 81k M Pro) was used to measure the temperature curve or the temperature development on the front of the steel sheet. A three-dimensional thermographic imaging method is used for this (P. Burgholzer, M. Thor, J. Gruber, and G. Mayr, J. Appl.
    / 26
    Phys. 121, 105102 (2017)), with which the image y (r) can be reconstructed as a spatial function r of the absorbing pattern, the folding of the absorbed light I (r) p (r) using the thermographic one shown in FIG. 1 (c) PSF h (r) takes place:
    y (r) = h (r) * [/ (r) · p (r)] + e (r) h (r - r ') I (r') p (r ') dr' + e (r) , (6)
    Where ε (r) indicates the noise (error) in the data, p (r) indicates the optical absorption of the absorbing patterns and I (r) is the illuminating luminous flux. The spatial variable r is described for the line pair pattern as a one-dimensional coordinate on the steel surface perpendicular to the lines (x-direction), and for two-dimensional patterns, like an asterisk, is the two-dimensional Cartesian coordinate pair (x and y direction) on the back of the steel sheet described.
    In the first exemplary embodiment (FIG. 2), four parallel lines on the 3 mm thick steel sheet with a distance of 2 mm, 1.3 mm, 0.9 mm and 0.6 mm and a thickness of 1 mm were used as the absorbent pattern ( Fig. 2 (a)). For structured lighting, 1 mm wide slits were cut in an aluminum foil at a distance of 10 mm each, and this slit mask was moved perpendicular to the lines in the x direction with a step width of 0.2 mm. The use of at least M = 55 different illumination patterns I1, I2, ..., Im ensures the illumination of all absorption lines in this exemplary embodiment. The illumination patterns and the absorber distribution are represented by discrete vectors Im, peR N , the N components denoting the pixel values of the camera at equidistant points. According to Eq. (6) is the measured signal from the focused converter y m = h * [I m · p] + e m for m = 1, ..., M (7)
    The aim is to calculate the absorber distribution p and, to a certain extent, the lighting pattern Im from the data. The product Hm ^ Im-p corresponds to the heating source assigned to the m / 26th speckle pattern. The heating sources Hm are (theoretically) clearly determined by the expansion equations (7). However, due to the poorly conditioned deconvolution with a smooth core, these uncoupled equations are error-sensitive and only provide reconstructions of low resolutions if they are solved independently and without suitable regularization. In order to obtain high-resolution reconstructions, it is proposed according to the invention to use a reconstruction algorithm which takes advantage of the fact that all Hm come from the same density distribution ρ, which are also sparse, called the IJOSP algorithm. "Iterative joint sparsity").
    Numerically, this can be minimized by the following
    Μ N m = l i = l + -7 M! min
    H (8) with the FISTA (fast iterative threshold algorithm - A. Beck and M. Teboulle “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems,” SIAM J. Imaging Sci. 2, 183-202 (2009)) , The first term in Eq. (8) is the data fitting term, the second term uses the thin population and equality of density distribution ρ and the last term is a stability term known from the Tikhonov regulation for general inverse problems. As with other regulation methods, 01 and 02 are regulation parameters that must be selected appropriately for the results shown in FIG. 4 (cd = 10 -2 and O2 = 5 * 10 -4 ). The term (9) producing the thin population and equality of the density distribution ρ provides minimized solutions that prefer thin population and equality of the density distribution ρ. First, for each pixel, the measured l 2/26
    Norm over all M different lighting patterns and then these positive values are added up (N pixels). This term favors blocked thin solutions, which means that it has a lower value for solutions that only deviate from zero in a few places, but also gets lower if these non-zero entries are in the same place for all lighting patterns.
    The measurement and reconstruction results for the four absorbing line pairs are shown below.
    3 (a) shows the measured surface temperature Ts (x, t) without using the slit mask at time t. Since the thickness of the steel sheet (3 mm) is short compared to the length of the line pairs (47 mm), the problem can be reduced to a two-dimensional heat diffusion problem. In the y direction, parallel to the line pairs, the mean value is recorded over, in this exemplary embodiment, 32 camera pixels in order to improve the SNR by a factor 32 from approximately 25.5 to 144 for Ts (x, t). 3 (b) and (c) show Ts (x, t) for two different lighting patterns. Fig. 3 (d) shows the corresponding two-dimensional thermographic reconstruction ym (x) for the two different lighting patterns m = 10 and m = 19 in Fig. 3 (b) and (c) and the reconstruction y (x) for the Average in Fig. 3 (a). For the IJOSP reconstruction algorithm to function properly, it is necessary that these reconstructions vary for different lighting patterns. The two-dimensional thermographic reconstruction increases the effective SNR by a factor that is equal to the square root of the pixels used. 320 camera pixels were used in the x direction, 6 pixels for 1 mm on the steel sheet. Therefore, the effective SNR is about 2580, which results in the thermographic PSF shown in Fig. 1 (c) at a depth of 3 mm.
    4 shows the reconstructions from the mean signal of all speckle patterns, which corresponds to the reconstructed signal without the slit mask. A Richardson-Lucy (R-L) deconvolution of this signal using the thermographic PSF / 26 in the lateral direction and the IJOSP reconstruction are compared. The IJOSP allows all line pairs to be resolved, even those with a distance of only 0.6 mm, while the Richardson-Lucy (RL) deconvolution of the mean signal resolves only the two line pairs with a distance of 1.3 mm and 2 mm can.
    5 shows the same reconstruction results for a two-dimensional star-shaped structure instead of parallel line pairs. For the creation of the individual lighting patterns, the slits of the aperture B were not only aligned in the y direction, but also inclined by ± 45 ° in the x-y plane. With 55 lighting patterns per slot orientation, this results in 165 lighting patterns for the two-dimensional star-shaped structure.
    In summary, the resolution for the line pairs could be improved using the IJOSP algorithm from 6 mm lateral resolution (Fig. 1 (c)) of the PSF to less than 1.6 mm (1 mm line width and 0.6 mm line spacing), which is one Improvement of the resolution by approximately a factor of four results. How is such a resolution possible if the information transport through the steel sheet is limited by the entropy production The theoretical framework of high-resolution is closely linked to the theory of data compression, which takes advantage of the inherent thin population of natural objects in a suitable mathematical basis. The amount of information that is transported through the steel sheet for structured lighting is the same as for homogeneous lighting and the solution of the linear inverse Eq. (6). The higher spatial frequencies of the object are shifted downward by frequency mixing of the lighting frequencies. For the reconstruction, the lighting is either known (SIM) or additional information about the depicted structure, including non-negativity or thin occupation, is used (blind SIM). For thermographic imaging, the thin population is often a good assumption even in real space, even without using a representation in another base. Cracks or pores are often thinly distributed in the sample volume.
    / 26
    For comparison, the line pattern ρ was taken into account taking known lighting patterns from Eq. (7) calculated using the least squares method. The results for known lighting patterns were no better than the results for unknown patterns when using IJOSP. In addition, three-dimensional high-resolution thermographic imaging using e.g. Speckle patterns for lighting are possible, in which the PSF is not evenly distributed over the region shown, but increases with depth.
    A light-scattering sample, for example biological tissue (Fig.
    6a, b), illuminated with a laser whose light penetrates into the tissue and is scattered. The laser pulse creates light and dark areas (laser speckles) due to interference of the scattered light. The size of these speckles depends on the light wavelength, the scattering properties of the sample and the depth of the incoming light. These speckle patterns, which are unknown in the interior of the sample, are the unknown structured lighting which is applied to certain structures, e.g. B. blood vessels in the tissue is absorbed and thus becomes a heat source. Through many such speckle patterns and their evaluation with the IJOSP algorithm, the light absorbing structure can be used as e.g. B. the blood vessels can be reconstructed from the infrared images from the surface with high resolution.
    For the thermographic reconstruction, instead of the PSF h (r) from Eq. (6) Measured time-dependent temperature signals TS (r, t) are used directly, which use H (r, t), where H then also includes the temperature profile of the heat diffusion over time.
    / 26
    patent attorneys
    Dipl.-Ing. Helmut Hübscher
    Dipl.-Ing. Gerd pretty
    Dipl.-Ing. Karl Winfried Hellmich
    Spittelwiese 4, 4020 Linz (41476) HEL
    权利要求:
    Claims (12)
    [1]
    1. Method of taking thermal images one under one
    Sample surface (P) arranged to be imaged structure (S) with a thermal imaging camera (K) recording the sample surface (P), a source (Q) of electromagnetic radiation for illuminating the structure to be imaged (S) and with an evaluation unit (A) for evaluating the Thermal imaging camera (K) recorded surface measurement data, characterized in that the structure (S) to be imaged is illuminated with an unknown structured illumination and thus heated for improved reconstruction, several images being used for evaluating the structure (S) and the structure (S) each Illumination is illuminated with a differently structured illumination and that a non-linear iterative evaluation algorithm is used to calculate the structure to be imaged (S) from the images taken with the thermal imager (K), which uses the thin occupation and the constant location of the heated structure for the differently structured lighting patterns.
    [2]
    2. The method according to claim 1, characterized in that for the time-dependent temperature signals TS (r, t) measured with the thermal imaging camera (K) for each pixel are converted into a virtual acoustic signal.
    [3]
    3. The method according to claim 2, characterized in that an ultrasound reconstruction method is used in a subsequent step to reconstruct the spatial function y (r) of the structure (S) from the virtual acoustic signal.
    18/26
    [4]
    4. The method according to claim 3, characterized in that in a further subsequent step an IJOSP algorithm, which requires a point response (PSF), is used for the thermographic reconstruction of the structure.
    [5]
    5. The method according to claim 4, characterized in that for each image recorded by the thermal imager a point response (PSF) is derived from the signal-to-noise ratio (SNR) and the distance of the structure to be imaged (S) from a surface, frequencies of the signal amplitudes of the point response (PSF) that fall below the noise level are set to zero.
    [6]
    6. The method according to claim 4, characterized in that the point response (PSF) is determined from the reconstruction of a small punctiform structure at a certain depth.
    [7]
    7. The method according to any one of claims 1 to 6, characterized in that the source (Q) of electromagnetic radiation is a coherent light source, a laser or a microwave.
    [8]
    8. The method according to any one of claims 1 to 6, characterized in that the source (Q) of electromagnetic radiation is a non-coherent light source which illuminates the structure to be imaged (S) via an aperture (B) and that different aperture settings per image structure the different Ensure lighting per picture.
    [9]
    9. Device for taking thermal images of a structure (S) arranged under a sample surface (P) with a thermal imager (K) for taking the sample surface (P), with a source (Q) of electromagnetic radiation for illuminating the structure (S) and with an evaluation unit (A) for evaluating the surface measurement data recorded by the thermal imaging camera (K), characterized in that the evaluation unit operates according to a method according to one of Claims 1 to 5.
    19/26
    [10]
    10. The device according to claim 9, characterized in that the thermal imaging camera (K) is directed against the sample surface (P) in such a way that it takes thermal images of the structure (S) to be imaged arranged under a sample surface (P) and that the source (Q) Electromagnetic radiation for illuminating the structure (S) is arranged on the side of the sample surface (P) opposite the thermal imager (K) and is directed against the structure (S) to be imaged.
    [11]
    11. The device according to claim 10, characterized in that between the source (Q) and the structure (S) a diaphragm (B) for structured illumination of the structure (S) is arranged, wherein the diaphragm (B) relative to the structure (S) is displaceable is led.
    [12]
    12. The device according to claim 9, characterized in that the thermal imaging camera (K) is directed against the sample surface (P) in such a way that it takes thermal images of the structure (S) to be imaged arranged under a sample surface (P) and that the source (Q) electromagnetic radiation for illuminating the structure (S) is arranged on the same side as the thermal imager (K) with respect to the sample surface (P) and is directed against the structure (S) to be imaged.
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    同族专利:
    公开号 | 公开日
    US20210255042A1|2021-08-19|
    CA3063278A1|2019-12-05|
    AT520007B1|2019-09-15|
    EP3625760A1|2020-03-25|
    WO2018209370A1|2018-11-22|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US7018094B1|1999-10-16|2006-03-28|Airbus Uk Limited|Material analysis|
    WO2006124977A2|2005-05-18|2006-11-23|Federal-Mogul Corporation|Transient defect detection algorithm|
    EP2743688A1|2012-12-17|2014-06-18|Thermosensorik Gmbh|Method and system for the examination of a sample by means of thermography|
    CN103258755A|2013-04-22|2013-08-21|哈尔滨工业大学|Flip-chip welding spot defect back view temperature measurement detecting method|DE102018124984A1|2018-10-10|2020-04-16|Friedrich-Schiller-Universität Jena|Method and device for high-resolution fluorescence microscopy|
    CN109900742B|2019-04-03|2019-12-17|哈尔滨商业大学|device and method for detecting debonding defect of carbon fiber composite material in linear and nonlinear frequency modulation hybrid excitation refrigeration mode|
    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    ATA50421/2017A|AT520007B1|2017-05-16|2017-05-16|thermography method|ATA50421/2017A| AT520007B1|2017-05-16|2017-05-16|thermography method|
    US16/613,986| US20210255042A1|2017-05-16|2018-05-02|Thermography method|
    PCT/AT2018/050007| WO2018209370A1|2017-05-16|2018-05-02|Thermography method|
    CA3063278A| CA3063278A1|2017-05-16|2018-05-02|Thermography method|
    EP18727129.1A| EP3625760A1|2017-05-16|2018-05-02|Thermography method|
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