  A physics-driven generative adversarial network (GAN) was established here
for single-pixel hyperspectral imaging (HSI) in the infrared spectrum, to
eliminate the extensive data training work required by traditional data-driven
model. Within the GAN framework, the physical process of single-pixel imaging
(SPI) was integrated into the generator, and the actual and estimated
one-dimensional (1D) bucket signals were employed as constraints in the
objective function to update the network's parameters and optimize the
generator with the assistance of the discriminator. In comparison to
single-pixel infrared HSI methods based on compressed sensing and
physics-driven convolution neural networks, our physics-driven GAN-based
single-pixel infrared HSI can achieve higher imaging performance but with fewer
measurements. We believe that this physics-driven GAN will promote practical
applications of computational imaging, especially various SPI-based techniques.
