Abstract
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 technique (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.
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Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. The source code to implement AuSamNet is available in Ref. [29].
29. W. Huang, F. Wang, X. Zhang, Y. Jin, and G. Situ, “AuSamNet,” GitHub (2023) https://github.com/SituLab/AuSamNet.
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