Distributed Compressive Sensing vs. Dynamic Compressive Sensing: Improving the Compressive Line Sensing Imaging System through Their Integration

Abstract

In recent years, a compressive sensing based underwater imaging system has been under investigation: the Compressive Line Sensing (CLS) imaging system. In the CLS system, each line segment is sensed independently; with regard to signal reconstruction, the correlation among the adjacent lines is exploited via the joint sparsity in the distributed compressive sensing model. Interestingly, the dynamic compressive sensing signal model is also capable of exploiting the correlated nature of the adjacent lines through a Bayesian framework. This paper proposes a new CLS reconstruction technique through the integration of these different models, and includes an evaluation of the proposed technique using the experiment dataset obtained from an underwater imaging test setup.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2015
Accession Number
ADA627155

Entities

People

  • Anni K. Vuorenkoski
  • Bing Ouyang
  • Frank M. Caimi
  • Fraser R. Dalgleish
  • Sue Gong
  • Weilin W. Hou

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Attenuation
  • Backscattering
  • Coding
  • Compressed Sensing
  • Compression Ratio
  • Data Acquisition
  • Decoding
  • Forward Scattering
  • High Resolution
  • Image Reconstruction
  • Measurement
  • Optical Properties
  • Radiative Transfer
  • Scattering
  • Turbidity

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML