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Learning-based adaptive under-sampling for Fourier single-pixel imaging
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DOI：10.1364/OL.486416.png)
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In this Letter, we present a learning-based method for efficient Fourier single-pixel imaging (FSI). Based on the auto-encoder, the proposed adaptive under-sampling tech-nique (AuSamNet) manages to optimize a sampling mask and a deep neural network at the same time to achieve both under-sampling of the object image's Fourier spectrum and high-quality reconstruction from the under-sampled measurements. It is thus helpful in determining the best encoding and decoding scheme for FSI. Simulation and experiments demonstrate that AuSamNet can reconstruct high-quality natural color images even when the sampling ratio is as low as 7.5%. The proposed adaptive under -sampling strategy can be used for other computational imaging modalities, such as tomography and ptychography. We have released our source code.& COPY; 2023 Optica Publishing Group
期刊
IF:
3.3
论文数:
4.0W
被引数:
7.6W
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Single-pixel imaging by means of Fourier spectrum acquisition傅里叶光谱采集的单像素成像.png)
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