Abstract

Info

Title:: Medical image filtering based on local total variation cartoon-texture decomposition

Author(s):: WANG Jing1; HAN Xue1; HUANGPU Caihong2; QIAO Yingxu1; SIMA Haifeng1; 1. College of Computer Science and Technology, He’nan Polytechnic University, Jiaozuo 454000, China; 2. College of Electrical Engineering and Automation, He’nan Polytechnic University, Jiaozuo 454000, China

Keywords:: ; Keywords: image filtering; cartoon-texture; local total variation; nonlocal means

Abstract:: Abstract: In view of the disadvantages of the direct use of global filtering method, blurring image edges and losing important texture information, a nonlocal regularization filtering method based on fast variation cartoon-texture decomposition is proposed. Firstly, the nonlinear local total variation is used as an indicator function of image decomposition for separating the cartoon structural component and texture vibration component of the image. Secondly, according to the outstanding repeatability and structural orientation of the vibration component, a nonlocal gradient regular term with preferable directional selectivity and block similarity matching is combined with split Bregman method to solve the nonlocal total variation minimization problem of the vibration component. Finally, the combination of the decomposed cartoon component and the vibration component after noise removal is used for obtaining the restored image. The experimental results show that the proposed algorithm has excellent recovery effects for medical images with fine structure and texture. The proposed method can be effectively applied to clinical diagnosis and subsequent segmentation.