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Difference of Convolution for Deep Compressive Sensing

  • Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep learning-based compressive sensing (DCS) has improved the single scale compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of Convolution-based multi-scale DCS (DoC-DCS). Unlike the multi-scale DCS based on a well-designed filter in the wavelet domain, our DoC-DCS jointly learns decomposition, sampling, and reconstruction thereby outperforms other state-of-the-art deep learning based CS methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages2105-2109
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period22/09/1925/09/19

Keywords

  • compressive sensing
  • deep learning
  • difference of convolution
  • difference of Gaussian

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