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Reference-based compressed sensing: A sample complexity approach

Book Contribution - Book Chapter Conference Contribution

We address the problem of reference-based compressed sensing: reconstruct
a sparse signal from few linear measurements using as
prior information a reference signal, a signal similar to the signal
we want to reconstruct. Access to reference signals arises in applications
such as medical imaging, e.g., through prior images of
the same patient, and compressive video, where previously reconstructed
frames can be used as reference. Our goal is to use the
reference signal to reduce the number of required measurements for
reconstruction. We achieve this via a reweighted ℓ1-ℓ1 minimization
scheme that updates its weights based on a sample complexity
bound. The scheme is simple, intuitive and, as our experiments
show, outperforms prior algorithms, including reweighted ℓ1 minimization,
ℓ1-ℓ1 minimization, and modified CS.
Book: IEEE International Conference on Acoustics, Speech, and Signal Processing
Pages: 1-5
Number of pages: 5
Keywords:Compressed sensing, reweighted ℓ1 minimization, prior information, sample complexity
  • ORCID: /0000-0001-9300-5860/work/71094947
  • WoS Id: 000388373404167
  • Scopus Id: 84973345367