Abstract
The main idea of RPCA (Robust Principal Component Analysis) is to decompose a matrix into a low-rank component L and a sparse component S. In dynamic MRI (Magnetic Resonance Imaging) reconstructions, due to the regular movement or pulsation of organs, the change is relatively slight and the similarity between frames is very high. Therefore, the RPCA model is very suitable for the reconstruction of dynamic MR images. However, for static images, their low-rank attribute is not obvious and we cannot achieve better results if we apply the RPCA model to the reconstruction of static images directly. Based on the non-local self-similarity and sparsity of an image, this paper successfully applied the RPCA model to the reconstruction of static MR images and achieved good reconstruction results.
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.
