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The article presents a method to estimate multiple phase maps from a moiré fringe signal obtained using digital holographic interferometry. The proposed method uses a unitary transformation based signal subspace approach, and shows high robustness against noise. In addition, the method facilitates the estimation of multiple phase maps in a single shot operation without the need for spectral filtering or multiple images. The computational efficiency of the method was improved by a high performance implementation using a graphics processing unit. The practical utility of the proposed method is demonstrated using simulation and experiment results.

Surface profile measurement at the nanoscale level has important applications in non-destructive testing and evaluation. The paper proposes a robust method for surface profilometry using digital holographic microscopy setup. The proposed method relies on extracting the phase map encoded in the hologram signal, which directly corresponds to the surface profile, using state space approach. The main advantage of the proposed method is high robustness against noise, which is demonstrated using numerical simulations. For designing the experimental system, a combination of Raspberry-Pi computer and camera module is used for hologram acquisition and processing, which is a step towards low cost imaging. For surface profile measurement, the experimental system has both temporal and spatial sensitivity parameters within 5 nanometers, which indicates robust design. In addition, experimental results show that the proposed method shows superior performance compared to the existing methods for measuring 100 nanometers surface profile features corresponding to different micro-structure regions of a standard calibration test target. Overall, the proposed method allows for single shot, non-contact and full-field measurement of nanoscale surface profile with additional benefits of noise robustness and low cost imaging design.

The article introduces a Kalman filter and naive Bayes classifier based machine learning model for defect identification in the noisy fringe pattern. The simulation results corroborate the performance of the proposed method.

In this article, we present deep learning approach to estimate displacement derivatives in digital holographic interferometry. The results show the capability of the proposed method on noisy experimental fringes.

In quantitative phase microscopy, measurement of the phase gradient is an important problem for biological cell morphological studies. In this paper, we propose a method based on a deep learning approach that is capable of direct estimation of the phase gradient without the requirement of phase unwrapping and numerical differentiation operations. We show the robustness of the proposed method using numerical simulations under severe noise conditions. Further, we demonstrate the method’s utility for imaging different biological cells using diffraction phase microscopy setup.

The paper introduces hidden grid background oriented schlieren for quantitative study and visualization of natural convection heat transfer. In this technique, the refractive index variation, induced by the temperature gradient, is encoded in the recorded signal phase through the distortion of a background pattern. The background (undistorted) pattern is implicit (or hidden) in the light source. Quantitative estimation of the phase map is obtained by windowed Fourier transform. This method offers localized processing of the signal using joint space-frequency representation. The performance of hidden grid background oriented schlieren is practically demonstrated by investigating natural convective flow, a demanding task due to its comparatively small heat transfer.

A fringe pattern normalization algorithm is proposed using the smoothing Kalman filter. The background intensity and the fringe amplitude are estimated consecutively. These estimates are subsequently utilized to obtain the normalized fringe pattern. The fringe pattern normalization can be performed by scanning it either in a row-column-wise or in a zigzag manner. The simulation and experimental results are provided to substantiate the practical applicability of the proposed method.

The paper presents a method for automated defect identification from fringe patterns. The method relies on computing the fringe signal’s Wigner–Ville distribution followed by a supervised machine learning algorithm. Our machine learning approach enables robust detection of fringe pattern defects of varied shapes and alleviates the limitations associated with thresholding-based techniques that require careful control of the threshold parameter. The potential of the proposed method is demonstrated via numerical simulations to identify different types of defect patterns at various noise levels. In addition, the practical applicability of the method is validated by experimental results.

The article presents an optimization approach to estimate phase derivatives using anisotropic total variational regularization in digital holography. The results validate the method’s performance in handling sharp variations.

In this article, an optimization framework using non convex, non smooth total generalized variation based regularization using surrogate principle is presented to mitigate the affects of fast varying phases and corruption of interferogram with noise and sudden amplitude changes.

Here, we present an effective defect identification method using phase deriva- tives as feature vectors and a naive Bayes classifier to predict defective pixels. Simulation results are shown to illustrate the performance of the proposed method.

In digital holographic interferometry, reliable estimation of phase derivatives from the complex interference field signal is an important challenge since these are directly related to the displacement derivatives of a deformed object. In this paper, we propose an approach based on deep learning for direct estimation of phase derivatives in digital holographic interferometry. Using a Y-Net model, our proposed approach allows for simultaneous estimation of phase derivatives along the vertical and horizontal dimensions. The robustness of the proposed approach for phase derivative extraction under both additive white Gaussian noise and speckle noise is shown via numerical simulations. Subsequently, we demonstrate the practical utility of the method for deformation metrology using experimental data obtained from digital holographic interferometry.

Fast and accurate measurements of surface profiles is of vital importance in the field of non-destructive material testing. In the article, we propose the application of a robust phase retrieval method in digital holographic microscopy for nanoscale surface profile measurement. The proposed method is based on a parameter estimation strategy using a unitary transformation procedure applied over a windowed region of the numerically reconstructed wavefield signal. The proposed method is suitable for phase extraction even in the presence of noise and non-uniform amplitude variations. The applicability of the proposed method is demonstrated using both simulation and experimental results.

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.

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.

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 paper proposes a high throughput and non-contact technique based on diffraction phase microscopy and space-frequency method for non-invasive measurement of displacement and displacement derivatives. The technique relies on common-path interferometric design and interferogram processing based on the windowed Fourier transform, and has inherent advantages such as high stability, good throughput, single shot operation and robustness against noise and external disturbances. The utility of the proposed technique is demonstrated for practical applications in surface metrology using experimental results.

We propose a method for rapid and simultaneous estimation of interference phase and its derivatives in digital holographic interferometry. The method is based on graphics processing unit (GPU) assisted implementation of state space approach for interferogram processing and provides a massive improvement in computational efficiency over the conventional sequential approach. As a result, the proposed method offers feasibility for dynamic non-contact deformation metrology. We validate the method’s application potential in digital holographic interferometry for analyzing dynamic deformation of a test object.

Non-contact measurement of displacement undergone by a deformed object is an important application problem in digital holographic interferometry. Such measurements usually demand reliable estimation of interference phase even in the presence of severe noise. This article describes a method for non-contact displacement testing by investigating a robust phase retrieval approach in digital holographic interferometry. The approach is based on eigenspace processing of the complex interference field signal in digital holographic interferometry. The performance of the proposed method for phase retrieval under severe noise conditions is illustrated using simulation results. The practical utility of the proposed method is demonstrated for dynamic deformation analysis using experimental data from digital holographic interferometry.

In digital holographic microscopy, direct measurement of phase gradient is commonly required for investigations pertaining to cell morphology and feature detection. In this paper, we present the application of the Wigner-Ville distribution method for single shot quantitative phase gradient measurement in digital holographic microscopy. The method allows simultaneous extraction of phase derivatives along the horizontal and vertical dimensions to compute the phase gradient without the need of multiple images, filtering operations, or specialized experimental setups. Experimental results for imaging onion and cheek cells and standard calibration target, validates the potential of the proposed approach in digital holographic microscopy.

The paper introduces a method for studying flow dynamics using diffractive optical element based background-oriented schlieren (BOS). Our method relies on fringe demodulation using root multiple signal classification technique which provides high robustness against noise. Further, a graphics processing unit (GPU) based implementation is proposed which offers significant improvement in computational efficiency, and thus enables high speed analysis of flows. The performance of the method is demonstrated via numerical simulations and the practical applicability is also shown by analyzing a diffusion phenomenon in liquids by BOS., Journal

This paper presents an optimization-based method for phase extraction from interferograms corrupted with noise, rapid phase variations, and localized amplitude fluctuations. In the proposed method, the phase retrieval problem is addresed by modeling a cost function using non-convex non-smooth total generalized variational regularization. Further, the surrogate principle is used to transform the cost function into convex form for convenient optimization framework. Simulation results demonstrate the performance of the method. We also show the experimental utility of the proposed method for onion cell imaging using digital holographic microscopy.

In this paper, we propose a fringe analysis method to address the problem of phase retrieval from interferogram corrupted with fringe amplitude non-uniformity, and show its practical application for nanoscale surface profilometry. The proposed method relies on the implementation of second order optimization combined with total variation regularization using graphic processing unit based computing. The capability of the proposed method is demonstrated via numerical simulations by inducing different amplitude non-uniformity patterns in the interferograms and through experimental non-invasive surface topography using diffraction phase microscopy.

Dynamic measurement of surface profile is an important requirement in nondestructive testing, especially for the inspection of large samples with consecutive area scans or test objects under translation. In this paper, we propose the application of an eigenspace signal analysis method in diffraction phase microscopy for reliable and noncontact dynamic surface metrology. We also propose the inclusion of a graphics processing unit (GPU) computing framework in our method to enable fast interferogram processing for dynamics-based investigations. The practical viability of the proposed method is demonstrated for noninvasive nanoscale topography of a test target.

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.