Color-Accurate Underwater Imaging using Perceptual Adaptive Illumination

Background:: Ultrasound is widely used in the applications of underwater imaging. Capacitive micromachined ultrasonic transducer (CMUT) is a promising candidate to the traditional piezoelectric ultrasonic transducer. In underwater ultrasound imaging, better resolutions can be achieved with a higher frequency ultrasound. Therefore, a CMUT array for high-frequency ultrasound imaging is proposed in this work. Methods:: Analytical methods are used to calculate the center frequency in water and the pull-in voltage for determining the operating point of CMUT. Finite element method model was developed to finalize the design parameters. The CMUT array was fabricated with a five-mask sacrificial release process. Results:: The CMUT array owned an immersed center frequency of 2.6 MHz with a 6 dB fractional bandwidth of 123 %. The pull-in voltage of the CMUT array was 85 V. An underwater imaging experiment was carried out with the target of three steel wires. Conclusion:: In this study, we have developed CMUT for high-frequency underwater imaging. The experiment showed that the CMUT can detect the steel wires with the diameter of 100 μm and the axial resolution was 0.582 mm, which is close to one wavelength of ultrasound in 2.6 MHz.

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Pruned Distributed and Parallel Subarray Beamforming for 3-D Underwater Imaging With Fine-Grid Sparse Arrays

IEEE Journal of Oceanic Engineering ◽

10.1109/joe.2021.3056705 ◽

2021 ◽

pp. 1-16

Author(s):

Dongdong Zhao ◽

Peng Chen ◽

Yingtian Hu ◽

Ronghua Liang ◽

Haixia Wang ◽

...

Keyword(s):

Underwater Imaging ◽

Fine Grid ◽

Sparse Arrays ◽

Subarray Beamforming

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Making the Invisible Visible: Underwater Malta—A Virtual Museum for Submerged Cultural Heritage

Remote Sensing ◽

10.3390/rs13081558 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1558

Author(s):

Timmy Gambin ◽

Kari Hyttinen ◽

Maja Sausmekat ◽

John Wood

Keyword(s):

Cultural Heritage ◽

Public Access ◽

Human Interaction ◽

Underwater Cultural Heritage ◽

Virtual Museum ◽

Underwater Imaging ◽

Human Occupation ◽

The Public ◽

3D Photogrammetry ◽

Underwater Sites

The seabed can be considered as the world’s largest museum, and underwater sites explored and studied so far provide priceless information on human interaction with the sea. In recognition of the importance of this cultural resource, UNESCO, in its 2001 Convention on the Protection of the Underwater Cultural Heritage, determined that objects/sites should be preserved in situ, whilst also advocating for public access and sharing. The implementation of these principles is not without difficulties. Some states have opened up underwater sites to the public—mainly through diving, yet the vast majority of the world’s population does not dive. In Malta, 7000 years of human occupation is reflected in and on the landscape, and recent offshore surveys show that the islands’ long and complex history has also left an indelible mark on the seabed. Besides difficulties related to their protection and management, these sites also present a challenge with regard to sharing and communicating. Recent advances in underwater imaging and processing software have accelerated the development of 3D photogrammetry of submerged sites and the idea for a virtual museum was born. The virtual museum, UnderwaterMalta, was created out of a need to share the plethora of underwater sites located on the seabed of the Maltese Islands. A multitude of digital tools are used to share and communicate these sites, offering visitors a dry dive into submerged sites that would otherwise remain invisible to the vast majority of the public. This paper discusses the basic principle of the sharing of underwater cultural heritage and the difficulties that beset the implementation of such a principle. A detailed explanation and evaluation of the methods used to gather the raw data needed is set in the context of the particular and unique working conditions related to deep water sites. The workings of this paper are based on first-hand experiences garnered through the recording of numerous wrecks over the years and the creation and launch of The Virtual Museum-Underwater Malta—a comprehensive virtual museum specifically built for “displaying” underwater archaeological sites that are otherwise invisible to the general public.

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Simulation of an active underwater imaging through a wavy sea surface

Journal of Modern Optics ◽

10.1080/09500340.2018.1429682 ◽

2018 ◽

Vol 65 (10) ◽

pp. 1210-1218 ◽

Cited By ~ 3

Author(s):

Ali Gholami ◽

Hossein Saghafifar

Keyword(s):

Sea Surface ◽

Underwater Imaging

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A Model-Based Comparison Of Underwater Imaging Systems

10.1117/12.945742 ◽

1988 ◽

Cited By ~ 16

Author(s):

Jules S. Jaffe ◽

Chris Dunn

Keyword(s):

Imaging Systems ◽

Underwater Imaging ◽

Model Based

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ITERATIVE REFINEMENT FOR UNDERWATER 3D RECONSTRUCTION: APPLICATION TO DISPOSED UNDERWATER MUNITIONS IN THE BALTIC SEA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w10-181-2019 ◽

2019 ◽

Vol XLII-2/W10 ◽

pp. 181-187 ◽

Cited By ~ 1

Author(s):

Y. Song ◽

K. Köser ◽

T. Kwasnitschka ◽

R. Koch

Keyword(s):

3D Reconstruction ◽

Baltic Sea ◽

Rapid Development ◽

Depth Map ◽

Ground Truth ◽

Iterative Refinement ◽

Underwater Imaging ◽

The Baltic Sea ◽

Dump Site ◽

The Baltic

<p><strong>Abstract.</strong> With the rapid development and availability of underwater imaging technologies, underwater visual recording is widely used for a variety of tasks. However, quantitative imaging and photogrammetry in the underwater case has a lot of challenges (strong geometry distortion and radiometry issues) that limit the traditional photogrammetric workflow in underwater applications. This paper presents an iterative refinement approach to cope with refraction induced distortion while building on top of a standard photogrammetry pipeline. The approach uses approximate geometry to compensate for water refraction effects in images and then brings the new images into the next iteration of 3D reconstruction until the update of resulting depth maps becomes neglectable. Afterwards, the corrected depth map can also be used to compensate the attenuation effect in order to get a more realistic color for the 3D model. To verify the geometry improvement of the proposed approach, a set of images with air-water refraction effect were rendered from a ground truth model and the iterative refinement approach was applied to improve the 3D reconstruction. At the end, this paper also shows its application results for 3D reconstruction of a dump site for underwater munition in the Baltic Sea for which a visual monitoring approach is desired.</p>

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Role of Restored Underwater Images in Underwater Imaging Applications

Applied System Innovation ◽

10.3390/asi4040096 ◽

2021 ◽

Vol 4 (4) ◽

pp. 96

Author(s):

Jarina Raihan A ◽

Pg Emeroylariffion Abas ◽

Liyanage C De Silva

Keyword(s):

Machine Learning ◽

Image Classification ◽

Feature Detection ◽

Sea Water ◽

Learning Algorithm ◽

Underwater Imaging ◽

Underwater Image ◽

Common Problems ◽

Restoration Method

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.

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