Open AccessSpatio‐temporal mean curvature based image sequence restoration
Author(s) -
Ren Fuquan,
Qiu Tianshuang
Publication year - 2016
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.2015.0246
Subject(s) - deblurring , image restoration , sequence (biology) , smoothness , image (mathematics) , artificial intelligence , mathematics , augmented lagrangian method , curvature , algorithm , mean curvature , mean curvature flow , computer science , computer vision , image processing , mathematical optimization , mathematical analysis , geometry , biology , genetics
In this study, the authors propose a restoration algorithm for blurred and noisy continuous image sequences. The proposed approach treats an image sequence as a space‐time volume and employs a spatio‐temporal mean curvature regularisation which is a novel regularisation proposed in the study to enhance the smoothness of the solution. An augmented Lagrangian method with splitting techniques is used to handle the problem, iteratively finding solutions to the subproblems. Experiments show that the proposed approach can produce higher quality results and more natural images comparing with other space‐time volume based methods on image sequence denoising and deblurring problems.