Field deployable dynamic lighting system for turbid water imaging

Gorman, Geoffrey Allen

doi:10.1575/1912/4835

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…For increasing greyscale contrast, homomorphic filtering [53,148] and histogram equalization [147] are useful. In highly turbid environments, exotic lighting techniques have been employed [59,75,73,62,95,117]. For restoring color, methods range from simple white balancing [23,113] to Markov Random Fields [167], fusion-based techniques [5], and even colored strobes [172].…”

Section: Underwater Imagingmentioning

confidence: 99%

“…Several more "exotic" methods utilizing unique hardware solutions are diagrammed in Figure 2-2. Light striping [59,62,73,117] and range gating [75] are both means of shrinking or eliminating, respectively, the volume of backscattering particles. Confocal imaging techniques have also been applied to see through foreground haze occlusions [95].…”

Section: High-turbidity Environmentsmentioning

confidence: 99%

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Computational strategies for understanding underwater optical image datasets

Kaeli¹

2013

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A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmited acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission.This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.

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Section: Underwater Imagingmentioning

confidence: 99%

Section: High-turbidity Environmentsmentioning

confidence: 99%

Computational strategies for understanding underwater optical image datasets

Kaeli¹

2013

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“…Other parameters are defined to assess image quality, include sometimes subjective criteria [23]. But, these parameters are not well suited to automatic target detection.…”

mentioning

confidence: 99%

Exploring underwater target detection by imaging polarimetry and correlation techniques

et al. 2013

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Underwater target detection is investigated by combining active polarization imaging and optical correlation-based approaches. Experiments were conducted in a glass tank filled with tap water with diluted milk or seawater and containing targets of arbitrary polarimetric responses. We found that target estimation obtained by imaging with two orthogonal polarization states always improves detection performances when correlation is used as detection criterion. This experimentally study illustrates the potential of polarization imaging for underwater target detection and opens interesting perspectives for the development of underwater imaging systems.

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A Review on the Underwater Image Restoration and Enhancement Methods

Wong¹,

Raveendran²,

Ong³

2021

ASMScJ

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Light scattering, as well as light absorption in the water, often cause underwater images to be hazy, poorly contrasted, and dominated by either green or blue colour cast. In this paper, we review some of the state-of-the-art approaches in which specifically designed to enhance the quality of the acquired images. These approaches are able to eliminate the color cast and haziness on the images as well as to improve the image colourfulness and contrast. The characteristics of each of the developed approaches are highlighted, and their performances are evaluated both subjectively and objectively by the quality assessment methods.

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Field deployable dynamic lighting system for turbid water imaging

Cited by 3 publications

References 44 publications

Computational strategies for understanding underwater optical image datasets

Computational strategies for understanding underwater optical image datasets

Exploring underwater target detection by imaging polarimetry and correlation techniques

A Review on the Underwater Image Restoration and Enhancement Methods

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