We have investigated the internal structure of materials by use of a digital acousto-optic holographic technique. The innovative technique is effective in detecting the internal structure information of functional gradient materials. The present visible-light-source-based digital holographic techniques are not able to detect the internal structure information of non-transparent objects and that motivates us to seek the ultrasound-based digital optical holography alternative. The characteristic model of functional gradient materials is modeled through finite element modeling software. The effects of material composition, shape and density on the ultrasound field distribution are simulated, which reveal the propagation pattern of the ultrasound in functional gradient materials as the shape, density and elastic modulus of the medium affect the acoustic properties. The dynamic changes of quantitative mapping relationship between the internal structural information and the sound field is also analyzed. Results indicate that the change is correlated with the internal structural information of the object. The simulation results provide useful quantifiable information for the subsequent research of digital acousto-optic holography to detect the internal structure of materials. Our research is applicable to various industrial problems where traditional digital holographic techniques alone fail to deliver solutions.
In this research, we propose the use of end-to-end deep learning simulation approach for assisting the design of LiDAR. The results show that two million points per second rate is optimal for point cloud based intersection classification task. The detection range of up to 100 meters corresponds to optimal classification performance. The 10 degree of upper field of view and 10 degree of lower field of view is sufficient for intersection classification. A linear increase of classification accuracy from 10 to 70 channels is evident. The research bridges the gap of lower level LiDAR simulation and development and self-driving visual tasks and expected to find applications to improve self-driving performance and safety.
Digital holographic imaging systems are promising as they provide 3-D information of the object. However, the acquisition of holograms during experiments can be adversely affected by the speckle noise in coherent digital holographic systems. Several different denoising algorithms have been proposed. Traditional denoising algorithms average several holograms under different experimental conditions or use conventional filters to remove the speckle noise. However, these traditional methods require complex holographic experimental conditions. Besides time-consuming, the use of traditional neural networks has been difficult to extract speckle noise characteristics from holograms and the resulting holographic reconstructions have not been ideal. To address tradeoff between speckle noise reduction and efficiency, we analyze holograms in the spectrum domain for fast speckle noise reduction, which can remove multiple-levels speckle noise based on convolutional neural networks using only a single hologram. In order to effectively reduce the speckle noise associated with the hologram, the data set of the neural network training cannot use the current popular image data set. To achieve powerful noise reduction performance, neural networks use multiple-level speckle noise data sets for training. In contrast to existing traditional denoising algorithms, we use convolutional neural networks in spectral denoising for digital hologram. The proposed technique enjoys several desirable properties, including (i) the use of only a single hologram to efficiently handle various speckle noise levels, and (ii) faster speed than traditional approaches without sacrificing denoising performance. Experimental results and holographic reconstruction demonstrate the efficiency of our proposed neural network.
In this research, we systematically investigated the image classification accuracy of Fourier Ptychography Microscopy (FPM). Multiple linear regression of image classification accuracy (dependent variable), PSNR and SSIM (independent variables) was performed. Notebly, results show that PSNR, SSIM, and image classification accuracy has a linear relationship. It is therefore feasible to predict the image classification accuracy only based on PSNR and SSIM. It is also found that image classification accuracy of the FPM is not universally significantly differed from the lower resolution image under the higher numerical aperture (NA) condition. The difference is yet much more pronounced under the lower NA condition.
This paper presents a simple and effective method, without the need for any additional recording of the intensity maps or tremendous iterative computations, to remove the complex zeroth-order term in the complex hologram for phase retrieval in two-step quadrature phase-shifting holography by utilizing the intensity in certain area in the complex hologram. We select a particular area in the complex hologram where there is negligible diffraction from the test sample to calculate the intensity. The calculated intensity value allows us to eliminate the complex zeroth-order term from the complex hologram. Exact phase distribution can then be reconstructed by using the new complex hologram without the zeroth-order and twin image noise. Experiment results have been performed to verify the effectiveness and feasibility of our proposed method.
The transport-of-intensity equation (TIE) is often used to determine the phase and amplitude profile of a complex object by monitoring the intensities at different distances of propagation or around the image plane. TIE results from the imaginary part of the paraxial wave equation and is equivalent to the conservation of energy. The real part of the paraxial wave equation gives the eikonal equation in the presence of diffraction. Since propagation of the optical field between different planes is governed by the (paraxial) wave equation, both real and imaginary parts need to be satisfied at every propagation plane. In this work, the solution of the TIE is optimized by using the real part of the paraxial wave equation as a constraint. This technique is applied to the more exact determination of imaging the induced phase of a liquid heated by a focused laser beam, which has been previously computed using TIE only. Retrieval of imaged phase using the TIE is performed by using the constraint that naturally arises from the real part of the paraxial wave equation.
Lossy and near-lossless digital hologram compression methods are investigated to compress different complexities of wafer surface structures. In the lossy compression method, we apply row- and column-based uniform downsampling together with spline interpolation, whereas in the near-lossless compression method, we use wavelet local modulus maxima and spline interpolation. Results have shown that the lossy compression method is able to achieve a compression ratio of up to 100 for simpler wafer surface structures than that for complex surface structures. However, the near-lossless compression method is able to yield almost lossless compression even for complex wafer surface structures with a compression of about two. The proposed compression methods are computationally friendly for wafer surface structures as there is no time-consuming iterative computation involved.