Compressive imaging using approximate message passing and a Cauchy prior in the wavelet domain

Paul R Hill, Jin-Hwan Kim, Adrian Basarab, Denis Kouamé, David Bull, Alin M Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)
294 Downloads (Pure)

Abstract

Approximate Message Passing (AMP) is an iterative reconstruction algorithm that performs signal denoising within a compressive sensing framework. We propose the use of heavy tailed distribution based image denoising, specifically using a Cauchy prior based Maximum A-Posteriori (MAP) estimate within a wavelet based AMP compressive sensing structure. The use of this MAP denoising algorithm provides extremely fast convergence for image based compressive sensing. The proposed method converges approximately twice as fast as the compared AMP methods whilst providing superior final MSE results over a range of measurement rates.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing (ICIP 2016)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2514-2518
Number of pages5
ISBN (Electronic)9781467399616
ISBN (Print)9781467399623
DOIs
Publication statusPublished - Mar 2017

Publication series

NameIEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2381-8549

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    Vision for the Future-Full

    Bull, D. R.

    1/02/1531/01/20

    Project: Research

    Cite this

    Hill, P. R., Kim, J-H., Basarab, A., Kouamé, D., Bull, D., & Achim, A. M. (2017). Compressive imaging using approximate message passing and a Cauchy prior in the wavelet domain. In 2016 IEEE International Conference on Image Processing (ICIP 2016) (pp. 2514-2518). (IEEE International Conference on Image Processing (ICIP)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2016.7532812