In this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machinelearning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool.
In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.
This paper presents a method for automated human detection using fisheye image. We introduce a probabilistic model to describe the wide variation of human appearance in hemispherical image. In our method, human is modeled as probabilistic templates of body silhouette and head-shoulder contour. These template features are extracted from the human images taken at various distance and orientation with respect to the camera, and form the training data set for model creation. A probabilistic appearance model is built by using the combination of principal component analysis (PCA) and kernel ridge regression (KRR). Finally, the problem of human detection is formulated as maximum a posteriori (MAP) estimation using above model. Experiments are conducted on indoor space where a fisheye lens camera is installed on the ceiling of crossing hallway. The feasibility and accuracy of our method is discussed through the experimental results.
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