Recently, low-dose computed tomography (CT) has become highly desirable because of the growing conce for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re-construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi-ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini-mization and sparse dictionary leaing to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.