Traditional Compressive Sensing (CS) image reconstruction methods suffer from high computational costs and low reconstruction quality, so deep learning models are widely used to achieve non-linear projection for better reconstruction. Recently, CS unfolding networks can combine the advantages of deep learning and traditional optimization methods, but existing methods are still limited by single-domain information flow within unfolding networks, leading to information loss during image-to-image mapping. This paper proposes an Optimization-inspired Multi-domain Enhanced Network (OME-Net) based on multi-domain collaboration and frequency-domain enhancement. At each stage of OME-Net, there are two parts: the gradient descent module and proximal operator. Rather than only adopting pure gradient descent formula, the proposed OME-Net uses multi-domain gradient descent module to synchronously extract multi-domain information for feature complementarity. The proximal operator is approximated by frequency-domain guided multi-resolution reconstruction architecture that enhances features on Fourier domain and fuses features to retain high-frequency details. Experimental results show that the proposed OME-Net significantly outperforms several traditional methods and deep learning methods for image CS reconstruction task.