Image super-resolution from compressed sensing observations

Wael Saafin, Miguel Vega, Rafael Molina, Aggelos K Katsaggelos

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

7 Scopus citations


In this work we propose a novel framework to obtain High Resolution (HR) images from Compressed Sensing (CS) imaging systems capturing multiple Low Resolution (LR) images of the same scene. The proposed CS Super Resolution (SR) approach combines existing CS reconstruction algorithms with an LR to HR approach based on the use of a Super Gaussian (SG) regularization term. The reconstruction is formulated as a constrained optimization problem which is solved using the Alternate Direction Methods of Multipliers (ADMM). The image estimation subproblem is solved using Majorization-Minimization (MM) while the CS reconstruction becomes an l1-minimization subject to a quadratic constraint. The performed experiments show that the proposed method compares favorably to classical SR methods at compression ratio 1, obtaining excellent SR reconstructions at ratios below one.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015


OtherIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City


  • compressed sensing
  • image enhancement
  • image reconstruction
  • image sampling
  • Super resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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