Advancing U.S. Navy Low-Light Underwater Operations

Abstract

U.S. Navy research on extremely low-light (ELL) cameras in nighttime underwater operations is limited. This study aims to address this limitation in capability by quantifying the Teledyne Bow tech Limited Explorer Pro Low Light Monochrome Cameras performance in the field as a function of water depth at night in the coastal ocean. To reach this goal, proven techniques like modulation transfer function (MTF) and contrast transfer function (CTF) analyses we reapplied to modified target patterns for lower-quality images. The new target pattern was tested on land using commercial cameras against a commercial test pattern chart for high-resolution cameras. The ELL camera vertical casts, including measures of surface lux and the water column characteristics, were performed at California's Monterey Harbor and Bay in the presence of bioluminescence. The MTF results from the target pattern showed a steady MTF as the spatial frequency increased; the MTF decayed with increasing depth and decreasing lux. Furthermore, the MTFs showed that bioluminescence improves the MTF at depths 24.5 m versus the MTF with no bioluminescence. The target pattern was detected at a maximum depth of 37 m. However, predicted maximum depths using a linear regression model were > 37m with and without bioluminescence.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2022
Accession Number
AD1184903

Entities

People

  • Miguel A. Green

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Advanced Materials
  • Cameras
  • Charge Coupled Devices
  • Computer Programs
  • Computers
  • Detection
  • Digital Images
  • Field Tests
  • Frequency
  • Frequency Response
  • High Resolution
  • International Organizations
  • Light Sources
  • Measurement
  • Military Operations
  • Night Vision
  • Optical Properties
  • Optics
  • Seabed
  • Standards
  • Two Dimensional

Readers

  • Coastal Oceanography
  • Image Processing and Computer Vision.
  • Mathematics or Statistics