In this paper, a single-input multiple-output (SIMO) channel model is introduced for the deconvolution process of ultrasound imaging; the ultrasound pulse is the single system input and tissue reflectivity functions are the channel impulse responses. A sparse regularized blind deconvolution model is developed by projecting the tissue reflectivity functions onto the null space of a cross-relation matrix and projecting the ultrasound pulse onto a low-resolution space. In this way, the computational load is greatly reduced and the estimation accuracy can be improved because the proposed deconvolution model contains fewer variables. Subsequently, an alternating direction method of multipliers (ADMM) algorithm is introduced to efficiently solve the proposed blind deconvolution problem. Finally, the performance of the proposed blind deconvolution method is examined using both computer simulated data and practical in vitro and in vivo data. The results show a great improvement in the quality of ultrasound images in terms of signal-to-noise ratio and spatial resolution gain.