Considering the survival rate of small animals and the continuity of the experiments, high-dose X-ray shooting process is not suitable for the small animals in computed tomography (CT) experiments. But the low-dose process results with images might be polluted by noises which are not conducive for the experiments. In order to solve this problem, we in this paper introduce a global dictionary learning based denoising method to apply the promotion of the low dose CT image. We at first adopted the K-means singular value decomposition (K-SVD) algorithm to train a global dictionary based on the high dose CT image. Then, the noise image could be decomposed into sparse component which was free from noise through the orthogonal matching pursuit (OMP) algorithm. Finally, the noise-free image could be achieved by reconstructing the image only with its sparse components. The experiments results showed that the method we proposed here could decrease the noise efficiently and remain the details, and it would help promote the low dose image quality and increase the survival rate of the small animals.
