In this article, a reliable method for surface topography is proposed employing a digital holographic microscopy setup. The proposed method is based on extracting the surface profile from the phase map encoded in the hologram signal. For hologram acquisition and reconstruction, we used a Raspberry Pi computer and camera module in our experimental setup, which is a step toward low-cost non-contact surface profilometry. A graphics processing unit (GPU) assisted state space method is used for rapid and reliable phase estimation, providing high robustness against noise. The experimental finding for a standard calibration target corroborates the practical viability of the proposed method.
Defect identification for quality control and industrial inspection necessitates the need for novel techniques in the field of non-destructive testing and experimental mechanics. Optical interferometric techniques are quite popular for non-destructive testing, and hence they are extensively used to locate and identify defects from fringe patterns. A rapid variation of the fringe density in the vicinity of the defect’s region serves as a means for the detection algorithms to identify them. With the advent of machine learning over recent years, it has paved way for algorithms with automatic detection, thereby, eliminating the problem of manual thresholding. In this paper, we propose an elegant technique which relies on computing windowed Fourier spectrum of the fringe pattern at a given spatial frequency and subsequently utilizing this spectrum with a GPU accelerated Support Vector Machine (SVM) algorithm for classification of a defect and a non-defect region. The windowed Fourier spectrum of the fringe pattern serves as a feature vector for the GPU accelerated SVM algorithm which internally performs a pixel classification, thereby producing a binary output of the defect. The performance of the proposed technique is tested on computer-generated fringe patterns at severe noise levels. A machine learning model is trained using the windowed Fourier spectrum of a 1024 x 1024 fringe pattern which is corrupted with an additive Gaussian noise at a signal to noise ratio of 5dB. The best set of hyper-parameters are deduced from the validation data and the proposed method is tested on the fringe patterns of size 1024 x 1024 which contain temporally varying defects. The results indicate that the proposed method is computationally efficient, robust against noise, and also capable of automating the defect identification problem.
Estimation of phase and its derivatives from fringe patterns, commonly referred to as fringe analysis is an important step in many non-destructive optical measurement techniques such as Fringe projection, Electronic speckle pattern interferometry (ESPI) and Digital holographic interferometry (DHI). Some of the applications of these techniques include deformation analysis, stress analysis, profilometry and defect detection. Here, the quantities of interest like displacement, stress and refractive index are encoded in the form of phase and phase derivatives in the recorded fringe patterns. In dynamic systems, large number of such fringe patterns are recorded necessitating the use of reliable and fast methods for fringe analysis. In this work, we proposed a GPU assisted subspace based method called multiple signal classification (MUSIC) for estimating both the phase and its derivatives. The method relies on noise subspace to calculate a polynomial equation whose roots are computed numerically. Both the phase and its derivatives are computed by selecting a relevant root according to the stability conditions. The performance of the proposed method was tested using 250 simulated fringe patterns with gradually increasing phase at signal to noise ratio of 5 dB. Collective processing of all 250 fringe patterns using Python’s Numpy library took approximately 14535 seconds whereas the graphics processing unit (GPU) had taken only 260 seconds, thus resulting in substantial reduction in execution time. These results show that the proposed method is robust against noise and the use of GPU makes it computationally very efficient.
Optical interferometric techniques have come to forefront in precision metrology applications such as surface profilometry, deformation analysis and defect testing. Reliable phase measurement from a recorded fringe pat- tern is the primary goal in most of these interferometric techniques. A primary constraint for accurate phase measurement is non uniform intensity fluctuations in fringe patterns caused by irregular illumination, uneven reflectivities and pixel defects. This problem is further aggravated in dynamics studies with multiple image capture where the intensity fluctuations can vary with time and thus lead to several fringe patterns getting affected. In this paper, this problem is investigated using the second order optimization framework along with the total variation regularization on a graphic processing unit (GPU). In our study, a series of fringe patterns are simulated with additive white Gaussian noise at signal to noise ratio of 20 dB. To induce fringe corruption due to the non uniform intensity conditions, the object wave amplitude is varied using an ellipse shaped filter which enables brighter conditions or stronger amplitudes inside the elliptical boundary and relatively darker conditions or weaker amplitudes outside of it. For time varying studies, we varied the size of the filter sequentially to gener- ate a series of interferograms with temporally varying non-uniform intensities. Further, orientations of elliptical illuminations are changed to verify the robustness of the algorithm. For processing a series of interferograms each with size of 512 by 512 pixels with dynamic range of 10 (ratio of brightest to darkest amplitude), we obtained root mean square error less than 0.25 radians for every interferogram using the proposed method. The results show that the proposed method is robust for handling non-uniform intensity corruptions in fringe patterns for dynamics based investigations.
Dynamic fault identification from fringe patterns is a challenging problem in optical metrology, and is required for applications such as non-invasive condition monitoring and fracture propagation . The paper addresses this problem by proposing a high speed technique for identifying temporally varying faults using graphics processing unit (GPU) accelerated Wigner-Ville distribution method. For this case, a huge stack of fringe patterns need to be processed and the parallel processing ability of GPU provides high computational efficiency and overall execution time improvement. For testing, we simulated a stack of 100 noisy fringe patterns containing time varying defects to mimic the temporal evolution of fracture in a test material. Each fringe pattern has size 4096 by 4096 pixels and signal to noise ratio of 5 dB, and thus the resulting image stack constitutes a large noisy data set. We demonstrate the performance of the proposed method for high speed detection of defects from the fringe patterns, and also show the comparative advantage of the GPU based parallel approach versus the conventional approach of sequential processing. For the given 16 megapixel image size, the sequential implementation using Python’s Numpy scientific library took about 38 minutes for processing a single fringe pattern whereas the same task could be completed within 4.5 minutes using the GPU based parallel implementation. Cumulatively, the reduction in computational cost for processing the complete fringe pattern data set would be substantial. Overall, our results show that the intensive and tedious task of dynamic fault detection can be efficiently processed using the proposed approach with high robustness against noise.
The study of defect dynamics is an important problem in the field of non-destructive testing, crack propagation and fracture mechanics. Optical interferometric techniques are extensively used for this purpose because of their non-invasive behaviour and full field operation. The fringe patterns obtained from these techniques serve as good indicators for finding defects. In dynamic defect analysis, a large number of fringe patterns are captured and processed. The regions where the fringe density varies significantly are classified as defects. Thus, a fast, reliable and robust algorithm for identifying the rapid variations of fringe density is required. In this paper, we propose a graphics processing unit (GPU) assisted space frequency method based on windowed Fourier spectrum analysis for processing the dynamically varying fringe patterns. The main advantage of this approach is high computational efficiency achieved using GPU computing framework. The performance of the proposed method is demonstrated using 100 simulated fringe patterns, each of size 2048 x 2048 pixels. This large data stack was efficiently processed using the proposed method within only a minute, and thus, the proposed method offers the feasibility of high speed defect analysis. The practical application of the proposed method is explored by processing the fringe patterns obtained from the propagation of micron sized defects in an experimental configuration based on common-path diffraction phase microscopy setup.
We present a new method, referred to as phase correlation imaging (PCI), to study cell dynamics and function through temporal phase correlation analysis. PCI offers label-free, high-performance, simple-design, as well as suitability for operation in a conventional microscopy setting. PCI works without the need for controlled or synchronized photoactivation and sophisticated acquisition schemes, and only involves taking a sequence of phase images. The PCI image incorporates information on the phase fluctuations induced by both Brownian motion and deterministic motion of intracellular transport across large scales. We employed spatial light interference microscopy (SLIM) recently developed in our laboratory to experimentally measure quantitative phase information which renders the thickness and refractive index of cellular components without adding contrast agents. The acquisition process is repeated to obtain time-lapse phase images. We calculate the correlation time at each pixel for acquired time-lapse phase images and obtain the correlation time map in space. By temporal correlation analysis, PCI reveals cell dynamics information, which is complementary to quantitative phase imaging itself.
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