Enhanced Blue View Imaging with Imporved Target SNR

Abstract

The two most popular technologies among new generation of high-frequency forward-look imaging sonars are the lens-based DIDSON and blazed array BlueView families, each with distinct properties. Having a narrower beam width of 0.3 [deg], DIDSON sonar systems provide finer details and resolution that are more suitable for analyzing smaller objects. Priced at about 1/3 of a DIDSON, the BlueView systems operating at dual frequencies of 0.9 and 2.25 MHz give inferior target details due to a larger horizontal beam width 1 [deg] . At the high end of 45-130 [deg] horizontal field of view with 0.9MHz operating frequency, they provide a coverage of about 90 meters (V) 50 meters (V) in a single image. Based on their current state, these systems are most suited for target detection and localization. This three-year project at a total cost of 315K aims to develop a novel algorithmic solution for constructing BlueView images with improved signal-to-noise ratio. These are generated by 1) aligning a number of relatively similar nearby viewpoints where the image appearance of the 3-D target does not change significantly; 2) accounting for the 3-D structure of scene objects for improved alignment; 3) fusing nearby views to construct super resolution images, namely, a solution higher than the native BlueView image. Our goal is to enable the automated recognition of small man-made targets by generating a number of super resolution views at distinct and diverse orientations, thus resolving ambiguity in 3-D object characterization based on single or small number of low-resolution images. Application for target classification and local-area terrain feature characterization will be explored using known object models. 1

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2016
Source ID
N000141512089

Entities

People

  • Shahriar Negahdaripour

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Miami

Tags

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.