A dictionary-learning-based image denoising algorithm is proposed in this paper. Since traditional methods seldom take into account the rotational invariance of dictionaries learned from image patches, an improved K-means singular value decomposition algorithm is developed. In our method, the rotational version of atoms is introduced to greedily match the noisy image in a sparse coding procedure. On the other hand, in a dictionary learning procedure, to maximize the diversity of atoms, a rotational operation on the residual error is adopted such that the rotational correlation among atoms is reduced. As the strategy exploits the rotational invariance of atoms, more intrinsic features existing in image patches can be effectively extracted. Experiments illustrate that the proposed method can achieve a better performance than some other well-developed denoising methods.