JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Mohammad Golbabaee, Pierre Vandergheynst

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

23 Citations (SciVal)

Abstract

In this paper we propose a novel and efficient model for compressed sensing of hyperspectral images. A large-size hyperspectral image can be subsampled by retaining only 3% of its original size, yet robustly recovered using the new approach we present here. Our reconstruction approach is based on minimizing a convex functional which penalizes both the trace norm and the TV norm of the data matrix. Thus, the solution tends to have a simultaneous low-rank and piecewise smooth structure: the two important priors explaining the underlying correlation structure of such data. Through simulations we will show our approach significantly enhances the conventional compression rate-distortion tradeoffs. In particular, in the strong undersampling regimes our method outperforms the standard TV denoising image recovery scheme by more than 17dB in the reconstruction MSE.
Original languageEnglish
Title of host publication2012 19th IEEE International Conference on Image Processing
PublisherIEEE
ISBN (Electronic)978-1-4673-2533-2
ISBN (Print)978-1-4673-2534-9
DOIs
Publication statusPublished - 21 Feb 2013

Publication series

NameIEEE International Conference on Image Processing
PublisherIEEE
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

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