Compressive sensing theory is applied to super-resolution imaging for the general sparsity of most images. Compressive sensing principle and simulation show that two dimensional image in transform domain has sparsity. The better are the sensing matrix characteristics, the better is the image reconstruction effect. Only 30% measured value about traditional image’s is adopted by compressive sensing sampling, the image quality equal to that of traditional sampling is restored. Comparing with gradient projection sparsity reconstruction (GPSR) and GPSR+TV algorithms, the effect of super-resolution image reconstruction with alternating direction method of multipliers (ADM) algorithm is better. And an optical imaging system with prism reflective compressive code aperture is proposed based on 4f optical system, in which spatial light modulator (SLM) is used as coded template to modulate and compress target images. Four times super-resolution reconstruction effect than that of CCD is realized through developed ADM algorithm software based on total variation sparsity reconstruction. Traditional imaging problems such as low image resolution, high pressure data storage and slow data transmission are resolved by the compressive sensing imaging technology, which has great application potential.