  Single-pixel imaging is a novel imaging scheme that has gained popularity due
to its huge computational gain and potential for a low-cost alternative to
imaging beyond the visible spectrum. The traditional reconstruction methods
struggle to produce a clear recovery when one limits the number of illumination
patterns from a spatial light modulator. As a remedy, several
deep-learning-based solutions have been proposed which lack good generalization
ability due to the architectural setup and loss functions. In this paper, we
propose a generative adversarial network-based reconstruction framework for
single-pixel imaging, referred to as SPI-GAN. Our method can reconstruct images
with 17.92 dB PSNR and 0.487 SSIM, even if the sampling ratio drops to 5%. This
facilitates much faster reconstruction making our method suitable for
single-pixel video. Furthermore, our ResNet-like architecture for the generator
leads to useful representation learning that allows us to reconstruct
completely unseen objects. The experimental results demonstrate that SPI-GAN
achieves significant performance gain, e.g. near 3dB PSNR gain, over the
current state-of-the-art method.
