Diffusion Model With Gradient Descent Module Guiding Reconstruction for Single-Pixel Imaging

Reconstructing high-quality images with few measurements has always been a primary goal for single-pixel imaging (SPI). Diffusion models have shown outstanding performance in image generation and have been effectively attempted in image reconstruction for ghost imaging. However, there is still a great deal of space for improvement in the quality of image reconstruction at low sampling rates. Inspired by the proximal gradient descent algorithm (PGD), we propose Diffusion Model with Gradient Descent Module Guiding Reconstruction for Single-Pixel Imaging. The gradient descent module in PGD is utilized for preliminary image reconstruction. The preliminary reconstruction serves as prior information to iteratively constrain the diffusion model, allowing it to generate target images consistent with the training data distribution. Additionally, the strong mapping ability of the diffusion model replaces the traditional proximal operator to accelerate convergence. Full connected sampling and convolutional sampling are proposed as alternative sampling methods to the traditional Gaussian random matrix sampling. Sampling and generation are optimized jointly to capture key image information and improve reconstruction accuracy. Simulations and experiments confirm that our proposed network can significantly improve the quality of image reconstruction at low measurement rates.