Abstract
In this paper, we present a novel and effective L2-gradient-flow-based semi-implicit finite-element method for solving a variational problem of image reconstruction. The method is applicable to several data scenarios, especially for the contaminated data detected from uniformly sparse or randomly distributed projection directions. We also give a complete and rigorous proof for the convergence of the semi-implicit finite-element method, in which the convergence does not rely on the choices of the regularization parameter and the temporal step size. The experimental results show that our method has more desirable performance comparing with other reconstruction methods in solving a number of challenging reconstruction problems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
