Objective Image Quality Metrics: Applications for Partially Compensated Images of Space Objects

Abstract

Digital image reconstruction tasks currently require human intervention for a subjective evaluation of image quality. A method for unsupervised measurement of digital image quality is desired. This research investigated various parameters (metrics) that can be automatically extracted from a digital image and tested how well they correlated with image quality. Specifically, images of orbiting satellites captured by a partially compensated adaptive optics telescope were dealt with. Two different types of quantities were investigated: (1) Fourier spectral parameters, based on the spatial- frequency sensitivities of the HVS; and (2) Histogram shape parameters (i.e image statistical moments) giving quantitative insight into the structural content, information content, and brightness distribution of an image. An atmospheric imaging simulator was used to generate a test database of images. The use of simulated imagery allowed precise control of the imaging parameters directly relating to image quality: (1) Root Mean Square Error; (2) Seeing conditions (Fried Parameter, ro); and (3) Target magnitude. This in turn allowed quantitative testing of candidate image quality metrics. Metrics could also be tested against the user defined parameters of the reconstruction process, as a proof-of-concept for totally unsupervised image reconstruction. Finally, based on this testing, two successful image quality metrics are recommended.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA273837

Entities

People

  • David J. Lee

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Satellites
  • Celestial Brightness
  • Databases
  • Detection
  • Detectors
  • Digital Images
  • Image Processing
  • Image Reconstruction
  • Information Theory
  • Low Earth Orbits
  • Pattern Recognition
  • Satellite Imaging
  • Signal Processing
  • Simulators
  • Space Objects
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Regression Analysis.

Technology Areas

  • Space
  • Space - Space Objects