Aiming at the problems of difficulty in detail recovery at low sampling rates, insufficient utilization of historical feature information and blocking artifacts in current image compressed sensing reconstruction methods based on deep unfolding networks, this paper proposes a diffusion and dual-attention alternating optimization network (DDANet) for image compressed sensing reconstruction. The network combines the diffusion model with the deep unfolding framework by designing an alternating iteration mechanism between the diffusion denoising sub-stage and dual-attention sub-stage, which achieves collaborative enhancement of data distribution priors and cross-stage feature fusion, improving the recovery quality at low sampling rates. The dual-attention sub-stage includes a cross-stage channel attention (CSCA) module and a window sparse attention (WSA) module, which enhance image reconstruction details by fusing cross-stage features and exploiting local self-similarity. To address blocking artifacts, this paper proposes a coordinate spatial self-correction (CSSC) module, which suppresses block boundary discontinuity artifacts through the collaborative action of spatial, coordinate, and self-calibration branches, ensuring global consistency of reconstructed images. Experimental results show that on the Set11 test set, when the sampling rate is 1%, the proposed method improves peak signal-to-noise ratio (PSNR） by 0.61 dB compared with diffusion message passing deep unfolding networks （DMP-DUN） which also uses diffusion model, and by 1.31 dB compared with optimization-inspired cross-attention transformer unfolding framework(OTCUF）; when the sampling rate is 10%, PSNR improves by 1.22 and 1.77 dB compared with OTCUF and unrolling fixed-point continuous network(UFC-Net） respectively, indicating that the method has significant advantages at low sampling rates.