Deep neural networks have led to the development of various data-driven architectures for image compressive sensing (CS), generally categorized into black-box models and deep unfolding networks (DUNs). DUN approaches have garnered attention due to their interpretability. However, most existing methods emphasize the design of iterative networks while insufficiently addressing the use of image priors. This study introduces a novel deep unfolding architecture for image CS, called GPD-CS, which integrates generative priors (GPs) and diffusion models (DMs). The proposed method involves a two-stage training process. In the first stage, a modified variational autoencoder is designed to capture compact prior representations of natural images, and a dynamic proximal mapping module is incorporated within the DUN to integrate prior information. During the second stage, the GP learning model's parameters are fixed, and a lightweight DM is employed to infer compact representations, which are then embedded into the iterative reconstruction process. Experiments validate the superior reconstruction performance of the proposed approach. The source code and pre-trained models are publicly accessible at: https://github.com/nkbourne/GPD-CS.

In recent years, deep unfolding networks (DUNs) have received widespread attention in the field of compressed sensing (CS) reconstruction due to their good interpretability and strong mapping capabilities. However, existing DUNs often improve the reconstruction effect at the expense of a large number of parameters, and there is the problem of information loss in long-distance feature transmission. Based on the above problems, we propose an unfolded network architecture that mixes Transformer and large kernel convolution to achieve sparse sampling and reconstruction of natural images, namely, a reconstruction network based on Transformer and convolution (TCR-Net). The Transformer framework has the inherent ability to capture global context through a self-attention mechanism, which can effectively solve the challenge of long-range dependence on features. TCR-Net is an end-to-end two-stage architecture. First, a data-driven pre-trained encoder is used to complete the sparse representation and basic feature extraction of image information. Second, a new attention mechanism is introduced to replace the self-attention mechanism in Transformer, and a hybrid Transformer and convolution module based on optimization-inspired is designed. Its iterative process leads to the unfolding framework, which approximates the original image stage by stage. Experimental results show that TCR-Net outperforms existing state-of-the-art CS methods while maintaining fast computational speed. Specifically, when the CS ratio is 0.10, the average PSNR on the test set used in this paper is improved by at least 0.8%, the average SSIM is improved by at least 1.5%, and the processing speed is higher than 70FPS. These quantitative results show that our method has high computational efficiency while ensuring high-quality image restoration.