Learning-based adaptive under-sampling for Fourier single-pixel imaging

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 (>24 dB) 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.