Publications

Matrix completion for compressive sensing using consensus equilibrium

Lee, Dennis J.

We propose a technique for reconstruction from incomplete compressive measurements. Our approach combines compressive sensing and matrix completion using the consensus equilibrium framework. Consensus equilibrium breaks the reconstruction problem into subproblems to solve for the high-dimensional tensor. This framework allows us to apply two constraints on the statistical inversion problem. First, matrix completion enforces a low rank constraint on the compressed data. Second, the compressed tensor should be consistent with the uncompressed tensor when it is projected onto the low-dimensional subspace. We validate our method on the Indian Pines hyperspectral dataset with varying amounts of missing data. This work opens up new possibilities for data reduction, compression, and reconstruction.