To measure large and heavy micro-structured workpieces in situ and improve the measurement accuracy, which strongly depends on the environment during the measurement of micro-structured surfaces, a workpiece rotating technique is proposed. This method utilizes the precise rotation of machine tools to drive the workpiece and records a set of elemental image arrays for the pickup stage to overcome the upper-resolution limit imposed by the Nyquist sampling theorem, which allows the increase in the two-dimensional spatial resolution of the computed depth images in integral imaging. By extracting the depth position, we can obtain accurate depth information and measurement results for micro-structured surfaces. We carried out simulations and experiments to demonstrate the proposed method, and the results show the feasibility of our method.
The study of imaging through scattering media especially 3D imaging is of great significance in many fields such as biomedical imaging. Recently, deep learning has been widely used in the field of information processing with its remarkable performance. In this paper, we proposed a method of three - dimensional imaging through scattering media based on deep learning. This method uses the deep neural network to process the information captured by the light field imaging system based on the microlens array, recovering the no-scattering 4D light field information, and then realize three-dimensional reconstruction by using the processed light field information. Deep learning method requires a large number of samples. But in many environments, it is difficult to obtain a large number of three-dimensional samples through experiment. To solve this crucial problem, we use incoherent light propagation model to simulate the light field propagation and generate samples which contains three-dimensional information through simulation. In this paper, we simulated the propagation of radiation emitted from objects behind a single layer of weak scattering media, generated a large number of samples of 4D light field information by simulation, trained the neural network and processed the test data set generated by simulation, and we realized the deblurring of the light field information which contains information of multiple layers of flat semitransparent objects, which could be used to realize the 3D reconstruction.
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