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

CAAI Transactions on Intelligence Technology

article

Rui Chen¹  Dong Pu²  Ying Tong¹  Minghu Wu³ 

[1] College of Information & Communication Engineering, Nanjing Institute of Technology;College of Electric Power Engineering, Nanjing Institute of Technology;Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of technology

关键词: singular value decomposition;    image representation;    image denoising;    optimisation;    correlation methods;   

DOI  :  10.1049/cit2.12044

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND