Enhanced Single Pixel Imaging by Using Adaptive Jointly Optimized Conditional Diffusion

Abstract

Single-pixel imaging can reconstruct the original image at a low measurement rate (MR), and the target can be measured and reconstructed in low-light environments by capturing the light intensity information using a single-photon detector. Optimizing reconstruction results at low MR has become a focal point of research aimed at enhancing measurement efficiency. The application of neural network has significantly improved reconstruction quality, but the performance still requires further enhancement. In this paper, a Diffusion Single Pixel Imaging Model (DSPIM) method is proposed. The conditional diffusion model is utilized in the training and reconstruction processes of single-pixel imaging and is jointly optimized with an autoencoder network. This approach simulates the measurement and preliminary reconstruction of images, which are incorporated into the diffusion process as conditions. The noises and features are learned through a designed loss function that consists of predicted noise loss and measurement accuracy loss, allowing the reconstruction to perform well at very low MR. Besides, an adaptive regularization coefficients adjustment method (ARCA) has been designed for more effective optimization. Finally, the learned weights are loaded into the single photon counting system as a measurement matrix, demonstrating that the blurriness caused by insufficient features at low MR is effectively addressed using our methods, resulting in clearer targets and well-distinguished features.