Compressed Sensing Super Resolution of color images

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

*Corresponding author for this work

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

5 Scopus citations


In this work we estimate Super Resolution (SR) images from a sequence of true color Compressed Sensing (CS) observations. The red, green, blue (RGB) channels are sensed separately using a measurement matrix that can be synthesized practically. The joint optimization problem to estimate the registration parameters, and the High Resolution (HR) image is transformed into a sequence of unconstrained optimization sub-problems using the Alternate Direction Method of Multipliers (ADMM). A new, simple, and accurate, image registration procedure is proposed. The performed experiments show that the proposed method compares favorably to existing color CS reconstruction methods at unity zooming factor (P), obtaining very good performance varying P and the compression factor simultaneously. The algorithm is tested on real and synthetic images.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9780992862657
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other24th European Signal Processing Conference, EUSIPCO 2016


  • Color images
  • Compressed Sensing
  • Image enhancement
  • Image reconstruction
  • Super Resolution

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Compressed Sensing Super Resolution of color images'. Together they form a unique fingerprint.

Cite this