Title:
CS-recovery-LevelSet-Normals
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Description: We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the image level curves and 2) reconstruction of an image fitting the normal vectors, the compressed sensing measurements and the sparsity constraint. The proposed technique can naturally extend to non local operators and graphs to exploit the repetitive nature of textured images in order to recover fine detail structures. In both cases, the problem is reduced to a series of convex minimization problems that can be efficiently solved with a combination of variable splitting and augmented Lagrangian methods, leading to fast and easy-to-code algorithms.
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File list (Check if you may need any files):
CS_TV.m
CS_TVN_it.m
CS_TVN_real_mex.c
CS_TV_real_mex.c
Dgraph.m
MRImask.m
NL_CS_TV.m
NL_CS_TVN.m
NL_estimate_Normals.m
ROF.m
TIP2253484.pdf
__MACOSX\._CS_TV.m
........\._CS_TVN_it.m
........\._Dgraph.m
........\._estimate_Normals.m
........\._example_run.m
........\._fgf.m
........\._MRImask.m
........\._NL_CS_TV.m
........\._NL_CS_TVN.m
........\._NL_estimate_Normals.m
........\._ROF.m
........\._snr.m
estimate_Normals.m
example_run.m
fgf.m
normal_wROF_mex.c
snr.m
license.txt
