000178722 001__ 178722
000178722 005__ 20190316235421.0
000178722 037__ $$aPOST_TALK
000178722 245__ $$aRobust joint reconstruction of misaligned images using semi-parametric dictionaries
000178722 269__ $$a2012
000178722 260__ $$c2012
000178722 336__ $$aPosters
000178722 520__ $$aWe propose a method for signal reconstruction in semi-parametric dictionaries. The proposed algorithm estimates both the signal decomposition and the intrinsic parameters of the dictionary during the reconstruction process. Some results about the convergence of the algorithm are presented. The method is used here for the joint reconstruction of a set of misaligned images. Experiments show that the proposed algorithm accurately recovers the set of images and is robust to occlusions and misalignments. This method may have interests in, e.g., non-dynamic cardiac MR imaging where one has access to only subsampled images of the heart at different positions.
000178722 6531_ $$aParametric dictionary
000178722 6531_ $$aMulti-view imaging
000178722 6531_ $$aCompressed sensing
000178722 700__ $$0242927$$aPuy, Gilles$$g179918
000178722 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000178722 7112_ $$aWorkshop on Sparsity, Localization and Dictionary Learning$$cLondon$$dJune 26, 2012
000178722 8564_ $$s3574595$$uhttps://infoscience.epfl.ch/record/178722/files/London-Robust_joint_reconstruction.pdf$$yn/a$$zn/a
000178722 909C0 $$0252276$$pLIFMET$$xU10984
000178722 909C0 $$0252392$$pLTS2$$xU10380
000178722 909CO $$ooai:infoscience.tind.io:178722$$pSB$$pSTI$$pposter$$qGLOBAL_SET
000178722 917Z8 $$x179918
000178722 917Z8 $$x179918
000178722 937__ $$aEPFL-POSTER-178722
000178722 973__ $$aEPFL$$sPUBLISHED
000178722 980__ $$aPOSTER