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

Rui Chen, Dong Pu, Ying Tong, Minghu Wu
2021 CAAI Transactions on Intelligence Technology  
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
more » ... 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.
doi:10.1049/cit2.12044 fatcat:in4xfi6ok5gqpiedbgoa2u5zua