One-Dimensional Multi-Frame Blind Deconvolution Using Astronomical Data for Spatially Separable Objects

Abstract

Blind deconvolution is used to complete missions to detect adversary assets in space and to defend the nation's assets. A new algorithm was developed to perform blind deconvolution for objects that are spatially separable using multiple frames of data. This new one-dimensional approach uses the expectation-maximization algorithm to blindly deconvolve spatially separable objects. This object separation reduces the size of the object matrix from an NxN matrix to two singular vectors of length N. With limited knowledge of the object and point spread function the one-dimensional algorithm successfully deconvolved the objects in both simulated and laboratory data.

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Document Details

Document Type
Technical Report
Publication Date
Mar 26, 2020
Accession Number
AD1094929

Entities

People

  • Marc R. Brown

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Satellites
  • Atmospheric Motion
  • Binary Stars
  • Charge Coupled Devices
  • Computational Science
  • Data Sets
  • Department Of Defense
  • Detectors
  • Governments
  • Low Earth Orbits
  • Space Force
  • Space Objects
  • Two Dimensional
  • United States Government

Readers

  • Image Processing and Computer Vision.
  • Linear Algebra

Technology Areas

  • Space
  • Space - Space Objects