Improving NIR single-pixel imaging: using deep image prior and GANs

We introduce a hybrid approach that combines deep image prior (DIP) with generative adversarial networks (GANs) to improve the resolution of single-pixel imaging (SPI). SPI excels in challenging conditions such as low light or limited spectral camera availability, particularly in the near-infrared (NIR) range from 850 to 1550 nm. By employing an unsupervised image super-resolution technique based on DIP, we reduce the need for extensive direct SPI image datasets. This innovation simplifies enhancing image quality in specific NIR bands. We provide numerical and experimental evidence to support our method and detail the enhancements in UNet and GAN architectures across four neural network configurations.