Abstract: Super resolution (SR) is considered as one of the “holy grails” of optical imaging and image processing. The introduction of compressive sensing theory presents a novel super-resolution reconstruction method from a single low-resolution image, which can avoid the requirements for the multiple sub-pixel images of traditional superresolution method. Analyzing the requirements of the similarities and differences between compressed sensing measurement matrices and optical imaging systems, a binary phase encoding compressive imaging method based on the 4-f optical architecture is presented, with the phase in the frequency domain randomly modulated, which can achieve super-resolution reconstruction from single low-resolution measurement images obtained with single exposure conditions, no other additional information collected. Binary phase mask is much easier to implement than random phase mask with uniform distribution, which is a more viable scheme for physical realization of compressive imaging. Simulation experiments demonstrate that the proposed method can effectively capture compressive measurements and implement super-resolution reconstruction in a single shot condition. Furthermore, another experiments show that this method is also more applicable to large-scale image reconstruction compared with random demodulation (RD) proposed by Romberg in the reconstruction time, and more practical in the sampling scheme than RecPC method proposed by Yin.