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Convex Recovery of Continuous Domain Piecewise Constant Images From Nonuniform Fourier Samples
Journal article   Open access   Peer reviewed

Convex Recovery of Continuous Domain Piecewise Constant Images From Nonuniform Fourier Samples

Greg Ongie, Sampurna Biswas and Mathews Jacob
IEEE transactions on signal processing, Vol.66(1), pp.236-250
01/01/2018
DOI: 10.1109/TSP.2017.2750111
PMCID: PMC6101269
PMID: 30140146
url
https://arxiv.org/pdf/1703.01405View
Open Access

Abstract

We consider the recovery of a continuous domain piecewise constant image from its nonuniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities/edges of the image are localized to the zero level set of a bandlimited function. This assumption induces linear dependencies between the Fourier coefficients of the image, which results in a two-fold block Toeplitz matrix constructed from the Fourier coefficients being low rank. The proposed algorithm reformulates the recovery of the unknown Fourier coefficients as a structured low-rank matrix completion problem, where the nuclear norm of the matrix is minimized subject to structure and data constraints. We show that the exact recovery is possible with high probability when the edge set of the image satisfies an incoherency property. We also show that the incoherency property is dependent on the geometry of the edge set curve, implying higher sampling burden for smaller curves. This paper generalizes recent work on the super-resolution recovery of isolated Diracs or signals with finite rate of innovation to the recovery of piecewise constant images.
Jacobian matrices Technological innovation Off-the-grid image recovery Convolution Image edge detection Heuristic algorithms finite rate of innovation Signal processing algorithms structured low-rank matrix completion Indexes

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