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. Moreover, this method greatly reduces system equivalent focal length and has a thin structure, which can provide theoretical guidance for the design and application of the micro-optical electromechanical system.