The traditional K-singular value decomposition (K-SVD) algorithm has poor imagedenoising performance under strong noise. An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization. First, a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary. The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary. Then, non-local regularity

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... is incorporated into the denoising model to further improve image-denoising performance. Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.