Diffraction gratings are among the essential devices for spreading and shaping beams in a wide range of optoelectronic and photonic sensors and fiber optic communications. This has triggered an interest towards inverse design and optimization of the parameters using gradient-based optimization, heuristic algorithms, and machine learning models. Approaches based on complex models (such as deep neural networks) provide enhanced robustness and rely on a huge amount of data to achieve accuracy. However, the generation of these data and multi-parameter optimization can be laborious and time-consuming with the Finite Difference Time Domain (FDTD) simulation. We present an optimization approach to obtain a single grating antenna with wide-angle emission for a photonic integrated flash Light Detection And Ranging (LIDAR) system. The device is simulated using a silicon nitride material operating at a wavelength of 905 nm. Our method relies on a supervised, data-centric approach in combination with a genetic algorithm optimization. Given an optimization and several parameters, we evaluate the variables based on their correlation with the merit function and reduce the search region consequently. This approach allows faster convergence and provides a flat field of view of (56.95°, 92.82°) at Full Width Half Maximum (FWHM) in one dimension simulation.
Three dimensional (3D) sensing has recently been introduced in mobile devices for mass-market applications such as facial or gesture recognition. The two technologies widely used in smartphones, structured light and time of flight, are based on active imaging and enable operating in darkness. Nevertheless, ocular security constraints limit the accessible outdoor range in sunlight conditions. In contrast, immunity to ambient light is a natural advantage of Frequency Modulated Continuous Wave (FMCW) LIght Detection And Ranging (LIDAR), which is also an active technique, using the coherent nature of the laser source, through a reference arm to amplify the signal from the scene. However, the FMCW LIDAR potentially requires complex opto-electronic components for sequential scanning of the scene. Therefore, obtaining high resolution images at video rate becomes a real issue. In this paper, we investigate low power FMCW imaging (~1mW) with instantaneous active illumination of the scene and heterodyne detection with an image sensor, to combine the advantages of previous techniques with each other. We consider several configurations for the optical system, common to the scene and reference beams. Ideal criteria include uniform illumination of the image sensor by the reference arm, wavefront matching of the scene and reference beams for an optimal heterodyne yield at every pixel location, and minimization of optical loss. Moreover, the average speckle size, with regards to the pixel size and numerical aperture influences the statistical performance of the whole sensor. Finally, the optimal system choice is reinforced by experimental results on a bench setup built in the visible range.
In this work, we propose a combination of the Teager–Kaiser energy operator (TKEO) and the spiral phase transform (SPT) for robust instant energy estimation of amplitude-modulated and frequency-modulated (AM–FM) signals, where the energy extraction is followed by a high-frequency component, generally considered as noise. We demonstrate that this noise component can be subtracted mathematically using the SPT transformation applied to the AM–FM signal. The improvement in demodulation is tested using a simulated AM–FM image and evaluated by the image quality index. An experimental speckle fringe pattern obtained by digital speckle pattern interferometry on a hard disk is denoised using a multiband approach and demodulated using the proposed method.
We present a Wiener Teager–Kaiser approach for phase derivative estimation from a single speckle correlation fringe. In principle, the Teager–Kaiser operator estimates the energy of the fringe pattern and extracts its phase derivatives using an energy separation algorithm. However, in the estimation of the energy, this operator presents a computation error mainly due to a high frequency component. In this work, we addressed this error in mean square error sense by applying the Wiener filter on the operator prior to phase derivative computation. The performance of our proposed method on simulated and real fringe improves significantly the accuracy of the Teager–Kaiser operator.
We propose a technique to estimate the phase derivative in both x and y directions based on Riesz transform from a single speckle correlation fringes. The originality of this technique is to exploit Riesz transform for phase derivatives estimation, spatial modulation, speckle denoising, and measure of features similarity. Phase modulation process is realized by combining a digital spatial carrier and Riesz quadrature; speckle denoising is computed using Riesz wavelets transform, and the performance is evaluated by Riesz features SIMilarity. Before applying our method on real speckle correlation fringes, its performance is tested by numerical simulation.
A method for optical phase extraction based on two-dimensional discrete wavelets transform (2-DWT) decomposition is shown. From modulated fringe pattern, phase distribution is extracted by the ratio between detail and approximation. Modulation process is realized digitally by introducing high-frequency spatial carrier, and this process needs two π/2-shifted fringe patterns. We propose to use only single fringe and generate its quadrature by spiral phase transform (SPT). After validation by computer simulation, we apply the 2-DWT algorithm on experimental speckle fringe correlation taken for hard disk surface. The extracted phase using SPT quadrature was compared with that given using this time experimental quadrature, and we show a good performance by multiscale structural similarity metric.
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