The invention relates to a combined medical science image fusion denoising method based on discrimination dictionary learning, which belongs to the technical field of digital image processing. Firstly, medical science images are collected. Secondly, the collected medical science images undergo training, obtaining a sparse dictionary and a low-rank dictionary. Thirdly, image decomposition is conducted on the sparse dictionary and the low-rank dictionary, obtaining sparse element and low-rank element. The sparse element is added with sparse constraint and weighted kernel norm constraint, and thelow-rank element is added with kernel norm, obtaining low-rank coefficient and sparse coefficient. Iteration is conducted on the low-rank dictionary and the low-rank coefficient, obtaining the updated low-rank component. Iteration is conducted on the sparse dictionary and the sparse coefficient, obtaining sparse component. Finally, the low-rank component and the sparse component are fused, obtaining the final fusion image. The invention is advantageous in that when source images have noise are input, the fusion effect can still be good, and thereby effect is obviously raised.