Deep image prior has been successfully applied to image compressed sensing, allowing capture implicit prior using only the network architecture without training data. However, existing methods fail to take full advantage of the characteristics of the different components of the image signal, resulting in loss of details, and the network architecture is designed in a homogeneous way, which limits the performance. We propose a novel network architecture to capture distinct implicit priors for different image components under the guidance of a designed loss function. In addition, we design a novel module to extract and fuse the local and global features to facilitate the interaction between the two components and boost the performance. Sufficient experiments demonstrate the competitive performance and effectiveness of our method.