Abstract: Multi-aperture imaging is a new imaging method combining with compound eye concept, which has a small size, large field of view, high-resolution images reconstruction and other advantages. However, due to the low resolution of sub-images, the improvements for the image resolution and field of view are very limited. A novel imaging method which could achieve both super-resolution and large field of view was proposed. The random coded mask was designed based on the framework of compressive sensing and placed on each sub-aperture. Instead of directly imaging and converging on the image sensor, the incident light field of each sub-aperture would be modulated by the coded mask. Then, the random projections of the input object could be acquired by the low-dimension image sensor within a single exposure. Finally, the sparse representation-based optimization algorithm was applied to reconstruct super-resolution and large field of view images from all low-resolution sub-images, which had more object pixels than the number of pixels of the image sensor. Both the theoretical model and simulation results show the feasibility of the proposed method.