Image‐denoising algorithm based on improved K‐singular value decomposition and atom optimization
Rui Chen, Dong Bing Pu, Ying Tong, Minghu Wu
Abstract
Abstract The traditional K‐singular value decomposition (K‐SVD) algorithm has poor image‐denoising 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.