Automated phase unwrapping in digital holography with deep learning

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between −π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications. © 2021 Optical Society of America1

Phase Unwrapping in the Presence of Strong Turbulence

Phase unwrapping in the presence of branch points using a least-squares wave-front reconstructor requires the use of a Postprocessing Congruence Operation (PCO). Branch cuts in the unwrapped phase are altered by the addition of a constant parameter h to the rotational component when applying the PCO. Past research has shown that selecting a value of h which minimizes the proportion of irradiance in the pupil plane adjacent to branch cuts helps to maximize performance of adaptive-optics (AO) systems in strong turbulence. In continuation of this objective, this research focuses on optimizing the PCO while accounting for the cumulative effects of the integral control law. Several optimizations are developed and compared using wave-optics simulations. The most successful optimization is shown to reduce the normalized variance of the Strehl ratio across a wide range turbulence strengths and frame rates, including decreases of up to 25 percent when compared to a non-optimized PCO algorithm. AO systems which depend on high, steady Strehl ratio values serve to benefit from these algorithms

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

A real-time multi-sensor 3D surface shape measurement system using fringe analysis

This thesis presents a state-of-the-art multi-sensor, 3D surface shape measurement system that is based upon fringe projection/analysis and which operates at speeds approaching real-time. The research programme was carried out as part of MEGURATH (www.megurath.org), a collaborative research project with the aim of improving the treatment of cancer by radiotherapy. The aim of this research programme was to develop a real-time, multi-sensor 3D surface shape measurement system that is based on fringe analysis, which provides the flexibility to choose from amongst several different fringe profilometry methods and to manipulate their settings interactively. The system has been designed specifically to measure dynamic 3D human body surface shape and to act as an enabling technology for the purpose of performing Metrology Guided Radiotherapy (MGRT). However, the system has a wide variety of other potential applications, including 3D modelling and visualisation, verbatim replication, reverse engineering and industrial inspection. It can also be used as a rapid prototyping tool for algorithm development and testing, within the field of fringe pattern profilometry. The system that has been developed provides single, or multi-sensor, measurement modes that are adaptable to the specific requirements of a desired application. The multi-sensor mode can be useful for covering a larger measurement area, by providing a multi-viewpoint measurement. The overall measurement accuracy of the system is better than O.5mm, with measurement speeds of up to 3 million XYZ points/second using the single-sensor mode and rising to up to 4.6 million XYZ points/second when measuring in parallel using the three sensor multi-sensor mode. In addition the system provides a wide-ranging catalogue of fringe profilometry methods and techniques, that enables the reconstruction of 3D information through an interactive user selection of 183 possible different paths of main combinations. The research aspects behind the development of the system are presented in this thesis, along with the author's contribution to this field of research, which has included the provision of a comprehensive framework for producing such a novel optical profilometry system, and the specific techniques that were developed to fulfil the aims of this research programme. This mainly included the following advanced methods: a transversal calibration method for the optical system, an adaptive filtering technique for the Fourier Transform Profilometry (FTP) method, and a method to synthetically restore the locations of the triangulation spots. Similarly, potential applications for the system have been presented and feasibility and accuracy analyses have been conducted, presenting both qualitative and quantitative measurement results. To this end, the high robustness levels exhibited by the system have been demonstrated (in terms of adaptability, accuracy and measurement capability) by performing extensive real experiments and laboratory testing. Finally, a number of potential future system developments are described, with the intention of further extending the system capabilities

An investigation of various computational techniques in optical fringe analysis.

Fringe projection is an optical technique for three dimensional non-contact measurement of height distributions. A fringe pattern is projected onto an object's surface and, when viewed off-axis, it deforms to follow the shape of the object. The deformed fringe pattern is analysed to obtain its phase, information that is directly related to the height distribution of the surface by a proportionality constant. This thesis analyses some key problems in fringe projection analysis. Special attention is focused on the automatisation of the process with Fourier Fringe Analysis (FFA). Unwrapping, or elimination of 21t discontinuities in a phase map, is treated in detail. Two novel unwrapping techniques are proposed, analysed and demonstrated. A new method to reduce the number of wraps in the resulting phase distribution is developed. A number of problems related to FFA are discussed, and new techniques are presented for their resolution. In particular, a technique with better noise isolation is developed and a method to analyse non-fullfield images based on function mapping is suggested. The use of parallel computation in the context of fringe analysis is considered. The parallelisation of cellular automata in distributed memory machines is discussed and analysed. A comparison between occam 2 and HPF, two compilers based upon a very different philosophy, is given. A case study with implementations in occam 2 and high performance FORTRAN (HPF) is presented. The advantages and disadvantages of each solution are critically assessed

Phase extraction of non-stationary signals produced in dynamic interferometry involving speckle waves

It is now widely acknowledged, among communities of researchers and engineers of very different horizons, that speckle interferometry (SI) offers powerful techniques to characterize mechanical rough surfaces with a submicronic accuracy in static or quasi-static regime, when small displacements are involved (typically several microns or tens of microns). The issue of dynamic regimes with possibly large deformations (typically several hundreds of microns) is still topical and prevents an even more widespread use of speckle techniques. This is essentially due to the lack of efficient processing schemes able to cope with non-stationary AM-FM interferometric signals. In addition, decorrelation-induced phase errors represent an hindrance to accurate measurement when such large displacements and classical fringe analysis techniques are considered. This work is an attempt to address those issues and to endeavor to make the most of speckle interferometry signals. Our answers to those problems are located on two different levels. First of all, we adopt the temporal analysis approach, i.e. the analysis of the temporal signal of each pixel of the sensor area used to record the interferograms. A return to basics of phase extraction is operated to properly identify the conditions under which the computed phase is meaningful and thus give some insight on the physical phenomenon under analysis. Due to their intrinsic non-stationary nature, a preprocessing tool is missing to put the SI temporal signals in a shape which ensures an accurate phase computation, whichever technique is chosen. This is where the Empirical Mode Decomposition (EMD) intervenes. This technique, somehow equivalent to an adaptive filtering technique, has been studied and tailored to fit with our expectations. The EMD has shown a great ability to remove efficiently the random fluctuating background intensity and to evaluate the modulation intensity. The Hilbert tranform (HT) is the natural quadrature operator. Its use to build an analytical signal from the so-detrended SI signal, for subsequent phase computation, has been studied and assessed. Other phase extraction techniques have been considered as well for comparison purposes. Finally, our answer to the decorrelation-induced phase error relies on the well-known result that the higher the pixel modulation intensity, the lower the random phase error. We took benefit from this result – not only linked to basic SNR considerations, but more specifically to the intrinsic phase structure of speckle fields – with a novel approach. The regions within the pixel signal history classified as unreliable because under-modulated, are purely and simply discarded. An interpolation step with the Delaunay triangulation is carried out with the so-obtained non-uniformly sampled phase maps to recover a smooth phase which relies on the most reliable available data. Our schemes have been tested and discussed with simulated and experimental SI signals. We eventually have developed a versatile, accurate and efficient phase extraction procedure, perfectly able to tackle the challenge of dynamic behaviors characterization, even for displacements and/or deformations beyond the classical limit of the correlation dimensions

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports