Single-pixel imaging based on a Fermat spiral laser array and an untrained neural network

We propose an efficient single-pixel imaging scheme that utilizes a Fermat spiral laser array and an untrained neural network. The Fermat spiral laser array serves as the illuminating light source, generating speckle light fields with nonperiodic spatial correlation properties. By projecting random speckles onto the object, a single-pixel detector captures the light intensities for image reconstruction. We introduce a model-driven untrained neural network (UNN) into the image reconstruction process. This deep learning method eliminates the need for pre-training on datasets and automatically optimizes the reconstructed image. Through experimental demonstration, we validate the superiority of the UNN method over traditional intensity correlation and compressive sensing algorithms in single-pixel imaging schemes based on laser arrays. In particular, the proposed single-pixel imaging (SPI) scheme successfully achieve high-quality image reconstruction for both binary and grayscale objects, even at a sampling ratio as low as 6.25%. Considering the laser array's potential for high emitting power, we believe that the current SPI method opens up avenues for practical applications such as remote sensing.