In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.

This paper describes an easy-to-implement three-dimensional (3-D) real-time shape measurement technique using our newly developed high-speed 3-D vision system. It employs only four projection fringes to realize full-field phase unwrapping in the presence of discontinuous or isolated objects. With our self-designed pattern generation hardware and a modified low-cost DLP projector, the four designed patterns can be generated and projected at a switching speed of 360 Hz. Using a properly synchronized high-speed camera, the high-speed fringe patterns distorted by measured objects can be acquired and processed in real-time. The resulting system can capture and display high-quality textured 3-D data at a speed of 120 frames per second, with the resolution of 640 × 480 points. The speed can be trebled if a camera with a higher frame rate is employed. We detail our shape measurement technique, including the four-pattern decoding algorithm as well as the hardware design. Some evaluation experiments have been carried out to demonstrate the validity and practicability of the proposed technique.

No abstract

Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value. INDEX TERMS Optical neural networks, optical waveguides, free-space optical interconnection, optical non-linearities, optical elements, photonic integrated circuits.

In this paper, we present a simple and effective scene-based nonuniformity correction (NUC) method for infrared focal plane arrays based on interframe registration. This method estimates the global translation between two adjacent frames and minimizes the mean square error between the two properly registered images to make any two detectors with the same scene produce the same output value. In this way, the accumulation of the registration error can be avoided and the NUC can be achieved. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of the proposed technique is thoroughly studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows a significantly fast and reliable fixed-pattern noise reduction and obtains an effective frame-by-frame adaptive estimation of each detector's gain and offset.

No abstract

Repeated surveys of selected lines from five trilateration networks along the San Andreas fault in southern California have been used to deduce the 1973–1984 strain accumulation records at five localities. The secular rate of engineering shear strain accumulation is about 0.3 μrad/yr with the plane of maximum shear parallel to the local strike of the San Andreas fault. The secular rate of accumulation of areal dilatation is negligible. The data were examined to detect evidence for fluctuations in the rate of strain accumulation. For this examination, 19 lines were removed from the data set: four because they exhibited an obvious coseismic offset and 15 others because they contained at least one very anomalous measurement that could reasonably be attributed to a survey blunder. (The incidence of such blunders appears to be one in every 75 measurements.) The remaining data consist of 104 lines with an average of 10 measurements each. Although the strain accumulation plots for the five networks may exhibit marginally significant temporal fluctuations, we are not convinced that those fluctuations are greater than could be attributed to survey error. In particular, we are unable to demonstrate that the 1973–1979 southern California strain anomaly reported by Savage and others is real. Given the uncertainty in the random and systematic errors in measurement, the strain measurements in southern California are marginally consistent with linear‐in‐time strain accumulation. The strain accumulation plots for the Salton network clearly established that, unlike the deformation reported after the 1940 Imperial Valley earthquake, no acceleration in the shear strain rate has yet been observed following the 1979 Imperial Valley earthquake.

In optical metrology, fringe projection is considered to be one of the most reliable techniques for recovering the shape of objects. For this technique, however, it is challenging to measure objects with a large variation in surface reflectivity, e.g. a scenario containing both dark and bright objects. Researchers have thus developed various approaches to high dynamic range (HDR) three-dimensional (3D) measurements over the years. In this paper, we present an overview of these techniques, as well as a new and definitive classification for them. We implement a set of representative techniques to measure objects with different characteristics of reflectance and discuss the advantages and constraints of the techniques according to the comparative results. Moreover, challenging problems and future research directions are discussed to advance HDR 3D measurement techniques.