On the basis of tensor-based theory applied to the high-dimensional image processing, a tensor-based sparse representation algorithm was proposed to preprocess lung CT images and to enhance the useful information for the images. Firstly, a Tensor-based Orthogonal Matching Pursuit (TOMP) was designed for sparse representation and a Higher-order K-Singular Value Decomposition (K-HOSVD) was constructed for updating the dictionary. Then, the main parameters of the tensor multiplication was optimized. It means that the 3D gray-level co-occurrence matrix was constructed and the relationship between tensor multiplication model and texture features was established. Finally, the proposed method was used in 150 sets of lung CT images from LIDC database and the sparse representation performance of the proposed scheme was evaluated. The results from proposed algorithm show that the Peak Signal to Noise Ratio (PSNR) is increased by 1.5% and the Mean Square Error (MSE) is decreased by 1.2% as compared with that of other common schemes. Furthermore, the edge bias error is decreased by 3.0% and the volume overlap is increased by 1.2% as compared with that of preprocessing segmentation result. The conclusion indicates that the proposed algorithm is more suitable for the sparse representation of three-dimensional images.