Open AccessNormal curvature‐induced variational model for image restoration
Author(s) -
Liu Pengfei,
Xiao Liang,
Li Tao
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0603
Subject(s) - hessian matrix , curvature , image restoration , norm (philosophy) , mathematics , rate of convergence , convergence (economics) , algorithm , image (mathematics) , mathematical optimization , computer science , image processing , computer vision , geometry , computer network , channel (broadcasting) , economic growth , political science , law , economics
In this study, a novel normal curvature‐induced variational model which involves a higher‐order regulariser based on the normal curvature prior information of image surface is proposed for image restoration. Furthermore, the authors derive a preferably equivalent formulation for the proposed normal curvature‐induced higher‐order regulariser. Then, they design an efficient algorithm to solve the proposed model by using the famous alternating direction method of multipliers technique. Finally, they assess the performance of the proposed method on both natural images and biomedical cell images by comparing it with the famous fast total variation (TV) method, fractional‐order TV method and Hessian‐nuclear‐norm regularisation method. Specifically, the proposed method can achieve better and more balanced results in terms of peak‐signal‐to‐noise ratio, convergence rate and restoration quality.