Spatial particle distribution can be recorded by holography technology and can be constructed from multi-layer hologram. Due to the influence of holographic recording and reconstruction process, each tomography of multi-layer reconstruction from holography also contains noise in addition to containing spatial particle distribution information. How to denoise each tomography is a key problem. The existing methods either have a long operation time or the noise reduction effect is not obvious. In order to solve the above problems, we proposed a denoising method based on deep learning in this paper. A deep neural network is built to train and test with simulated spatial particle tomography on multi-layer holography reconstruction. According to the simulation results, the method proposed in this paper is effective in denoising the reconstruction results of spatial particles. The proposed method has the advantages of rapidity and high efficiency.
We present multi-exposure compressive in-line holography (MCIH), which applies compressive holography to multi-exposure in-line holograms. Multi-exposure holograms of 4D moving object can be captured continuously at different time by in-line hologram recording setup. A new single hologram is generated by coded multi-exposure holograms uniformly. 4D moving object can be reconstructed by the new single hologram. And then the trajectory of moving object can be tracked, and the location of time-space object can be defined. The method is verified by two experiments. The results of experiments show that MCIH can obtain the time-varying location of moving object.
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