Paper
12 June 2023 Edge detection metrics for improved object recognition with underwater laser images
Author Affiliations +
Abstract
Active illumination with underwater laser imaging has unique advantages for the identification of underwater objects, especially in shallow waters, complex marine environments and inaccessible locations. Laser intensity images embody valuable information that can be utilized for object recognition; however, backscattered light from the water column and other particulates blur the resulting laser images, rendering the objects in the images unintelligible. Although over the years a variety of deblurring and other image restoration and enhancement algorithms have been proposed, these works primarily consider optical images of scenery, not monotone underwater images of objects, for which contours are more critical. This work proposes the utilization of edge metrics to evaluate the efficacy of image restoration and enhancement algorithms for underwater laser images. Our results provide insight into the best methods for improving underwater laser image quality for object recognition.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oladipupo Adeoluwa, Sevgi Z. Gurbuz, Carson Moseley, Anirban Swakshar, Patrick Kung, and Seongsin M. Kim "Edge detection metrics for improved object recognition with underwater laser images", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430T (12 June 2023); https://doi.org/10.1117/12.2663864
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KEYWORDS
Image quality

Image enhancement

Image processing

Object recognition

Histograms

Image restoration

Visualization

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