Space-time feature-specific imaging

Vicha Treeaporn, Amit Ashok, Mark A. Neifeld

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


Feature-specific imaging (FSI) or compressive imaging involves measuring relatively few linear projections of a scene compared to the dimensionality of the scene. Researchers have exploited the spatial correlation inherent in natural scenes to design compressive imaging systems using various measurement bases such as Karhunen-Loève (KL) transform, random projections, Discrete Cosine transform (DCT) and Discrete Wavelet transform (DWT) to yield significant improvements in system performance and size, weight, and power (SWaP) compared to conventional non-compressive imaging systems. Here we extend the FSI approach to time-varying natural scenes by exploiting the inherent spatio-temporal correlations to make compressive measurements. The performance of space-time feature-specific/compressive imaging systems is analyzed using the KL measurement basis. We find that the addition of temporal redundancy in natural time-varying scenes yields further compression relative to space-only feature specific imaging. For a relative noise strength of 10% and reconstruction error of 10% using 8×8×16 spatio-temporal blocks we find about a 114x compression compared to a conventional imager while space-only FSI realizes about a 32x compression. We also describe a candidate space-time compressive optical imaging system architecture.

Original languageEnglish (US)
Title of host publicationVisual Information Processing XX
StatePublished - 2011
EventVisual Information Processing XX - Orlando, FL, United States
Duration: Apr 26 2011Apr 27 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherVisual Information Processing XX
Country/TerritoryUnited States
CityOrlando, FL


  • Compressive imaging
  • Compressive sensing
  • Computational imaging
  • Imaging systems

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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