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Medical diagnosis needs a lot of medical image processing, due to the limitations of imaging technology and imaging time, the medical diagnosis is not able to get the clear image, which is necessary to reconstruct the medical image that have been acquired in the existing technology and considerably short time with super-resolution methods. Example-Based image super-resolution is to reconstruct the high-frequency (HF) details of the image from the prior model. HF will be estimated is considered as a combination of two components:main high-frequency(MHF) and residual high-frequency (RHF) ,this paper proposed a medical image super-resolution using dual-dictionary learning and sparse representation, which makes of the main dictionary and the residual dictionary learning recovering the MHF and RHF, respectively. Experimental results on test image show that by performing the proposed two-layer progressive method, more image details can be recovered and much better results can be achieved than that of existing methods.