Image-denoising algorithm based on improved K-singular value decomposition and atom optimization

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 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.