Regarding to the total generalized variational model cannot fully utilize the self-similarity information of the image structure when reconstructing images, an improved generalized variational image reconstruction model under non-local constraints is established to improve the quality of image reconstruction in the sparse sampling situation. This model introduces a non-local self-similarity of the transform domain as a priori information for image reconstruction. And the multi-directional total generalized variational regularization constraint is calculated in the eight-neighborhood space to protect the structural characteristics of the image. Further, the augmented Lagrangian theory is used to remove the constraint and solve the model, and an image reconstruction algorithm based on the improved total generalized variation is proposed. Simulation experimental results show that the proposed reconstruction model and image reconstruction algorithm can effectively remove the artifacts and noise in the image and meet the requirements of image reconstruction quality under sparse sampling condition. Compared with the famous reconstruction algorithms, the images reconstructed by proposed algorithm has significant improvement in both subjective visual effects and all objective evaluation indicators.