Abstract: Compressed sensing technology has been widely applied in the field of image acquisition and reconstruction,with its core objective being the accurate and high-quality reconstruction of images from a significantly reduced number of measurements.Compared to conventional signal processing approaches,deep learning-based image compressed sensing demonstrates superior performance in terms of reconstruction quality while also achieving reduced computational costs.Nevertheless,enhancing reconstruction accuracy under extremely low sampling rates remains a critical challenge.To address this issue,this paper proposes a novel deep unfolding framework named MambaCS,which significantly improves the performance of image compressed sensing through several innovative architectural components.During the sampling phase,a block-based measurement strategy is adopted to effectively balance computational complexity and sampling efficiency.In the reconstruction phase,the residual state space mo-dule is introduced,leveraging the advantages of Mamba in capturing long-range dependencies to better model complex spatial structures within images.Furthermore,in order to fully utilize the latent information contained in the measurement data,a multi-channel reuse block is incorporated into the reconstruction process.This module integrates multi-scale feature fusion with measurement reuse techniques,enhancing the network's ability to extract and represent key image features.Extensive experiments de-monstrate that MambaCS consistently outperforms existing state-of-the-art methods in terms of reconstruction accuracy and vi-sual quality.

Key words: Compressive sensing, Deep learning, Mamba, Image reconstruction, State space model