Compressive image sampling with side information

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

34 Citations (Scopus)


Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the discrete cosine transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for magnetic resonance imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used
Original languageEnglish
Title of host publicationImage processing (ICIP) 2009
Subtitle of host publicationproceedings of the 16th IEEE international conference
Place of PublicationNew York
Number of pages4
ISBN (Print)9781424456536
Publication statusPublished - 7 Nov 2009
Event16th IEEE International Conference on Image Processing (ICIP), 2009 - Cairo, Egypt
Duration: 7 Nov 200912 Nov 2009


Conference16th IEEE International Conference on Image Processing (ICIP), 2009


  • image processing
  • image reconstruction
  • compressive image sampling
  • side information
  • data mining
  • video compression
  • matching pursuit algorithms
  • magnetic resonance imaging
  • layout
  • iterative decoding
  • iterative algorithms
  • image sampling
  • image coding
  • discrete cosine transforms


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