Generalized k-level cutset sampling and reconstruction

Shengxin Zha, Thrasyvoulos N Pappas

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

1 Scopus citations

Abstract

We propose a family of cutset sampling schemes and a generalized k-level image reconstruction approach formulated under a minimum mean squared error (MMSE) framework. The k-level reconstruction approach is a direct generalization of the recently proposed pattern-based approach, and can be applied to periodic samples either on a cutset or on a grid. Our experimental results indicate that the generalization of the k-level reconstruction approach results in only a small performance loss. For rectangular cutsets, we show that the proposed approach outperforms the cutset-MRF approach as well as two inpainting approaches. Moreover, we show that combining the cutset sampling with an additional point sample inside the periodic structure outperforms k-level reconstruction from cutset sampling and point sampling under comparable sampling densities.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1681-1685
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • cutset
  • reconstruction
  • sampling

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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