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- W4225548944 abstract "In this paper, we present a method for speckle pattern design using deep learning. The speckle patterns possess unique features after experiencing convolutions in Speckle-Net, our well-designed framework for speckle pattern generation. We then apply our method to the computational ghost imaging system. The standard deep learning-assisted ghost imaging methods use the network to recognize the reconstructed objects or imaging algorithms. In contrast, this innovative application optimizes the illuminating speckle patterns via Speckle-Net with specific sampling ratios. Our method, therefore, outperforms the other techniques for ghost imaging, particularly its ability to retrieve high-quality images with extremely low sampling ratios. It opens a new route towards nontrivial speckle generation by referring to a standard loss function on specified objectives with the modified deep neural network. It also has great potential for applications in the fields of dynamic speckle illumination microscopy, structured illumination microscopy, x-ray imaging, photo-acoustic imaging, and optical lattices." @default.
- W4225548944 created "2022-05-05" @default.
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- W4225548944 date "2021-12-25" @default.
- W4225548944 modified "2023-09-26" @default.
- W4225548944 title "Deep-learned speckle pattern and its application to ghost imaging" @default.
- W4225548944 doi "https://doi.org/10.48550/arxiv.2112.13293" @default.
- W4225548944 hasPublicationYear "2021" @default.
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