Single-Pixel Hyperspectral Imaging via an Untrained Convolutional Neural Network

Single-pixel hyperspectral imaging (HSI) has received a lot of attention in recent years due to its advantages of high sensitivity, wide spectral ranges, low cost, and small sizes. In this article, we perform a single-pixel HSI experiment based on an untrained convolutional neural network (CNN) at an ultralow sampling rate, where the high-quality retrieved images of the target objects can be achieved by every visible wavelength of a light source from 432 nm to 680 nm. Specifically, we integrate the imaging physical model of single-pixel HSI into a randomly initialized CNN, which allows the images to be reconstructed by relying solely on the interaction between the imaging physical process and the neural network without pre-training the neural network.