Imaging through the atmosphere:Practical instrumentation-based theory and verification of aerosol modulation transfer function

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71 Scopus citations


Recent experimental measurements of overall atmospheric modulation transfer function (MTF) indicate a significant difference between overall atmospheric and turbulence MTF’s except often at midday, when turbulence is strong. We suggest a physical explanation for these results that essentially relates to a practical instrumentation-based atmospheric aerosol MTF that is a modification of the classical aerosol MTF theory. Based on radiative transfer theory, this practical approach takes into account the effect of finite field of view, finite dynamic sensitivity, and finite spatial bandwidth of every existing imaging system. These generally limit the scattering angles of received light to values far less than the diffraction limit for aerosols, thereby decreasing blur radius and increasing spatial frequency bandwidth. This can explain the broadening of the aerosol MTF from that theoretically expected. We discuss the asymptote value that the measured aerosol MTF approaches at high spatial frequencies, which is significantly higher than the theoretical prediction of turbid medium transmittance. An important conclusion that we derive is that the aerosol MTF is often the dominant part in the actual overall atmospheric MTF. In addition, there seems to be an inescapable trade-off between image resolution and image irradiance. The system designer must choose between imaging of faint and bright objects at the expense of image quality or imaging of either faint or bright objects with improved image quality. The concepts here are basic to all long-range imaging through the atmosphere.

Original languageEnglish
Pages (from-to)172-179
Number of pages8
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number1
StatePublished - 1 Jan 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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