Non-local compressive sampling recovery

Xianbiao Shu, Jianchao Yang, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Compressive sampling (CS) aims at acquiring a signal at a sampling rate below the Nyquist rate by exploiting prior knowledge that a signal is sparse or correlated in some domain. Despite the remarkable progress in the theory of CS, the sampling rate on a single image required by CS is still very high in practice. In this paper, a non-local compressive sampling (NLCS) recovery method is proposed to further reduce the sampling rate by exploiting non-local patch correlation and local piecewise smoothness present in natural images. Two non-local sparsity measures, i.e., non-local wavelet sparsity and non-local joint sparsity, are proposed to exploit the patch correlation in NLCS. An efficient iterative algorithm is developed to solve the NLCS recovery problem, which is shown to have stable convergence behavior in experiments. The experimental results show that our NLCS significantly improves the state-of-the-art of image compressive sampling.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Computational Photography, ICCP 2014
PublisherIEEE Computer Society
ISBN (Print)9781479951888
DOIs
StatePublished - Jan 1 2014
Event2014 6th IEEE International Conference on Computational Photography, ICCP 2014 - Santa Clara, CA, United States
Duration: May 2 2014May 4 2014

Publication series

Name2014 IEEE International Conference on Computational Photography, ICCP 2014

Other

Other2014 6th IEEE International Conference on Computational Photography, ICCP 2014
CountryUnited States
CitySanta Clara, CA
Period5/2/145/4/14

    Fingerprint

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

Cite this

Shu, X., Yang, J., & Ahuja, N. (2014). Non-local compressive sampling recovery. In 2014 IEEE International Conference on Computational Photography, ICCP 2014 [6831806] (2014 IEEE International Conference on Computational Photography, ICCP 2014). IEEE Computer Society. https://doi.org/10.1109/ICCPHOT.2014.6831806