Exploring the Seafloor with Underwater Robots

García, Rafael; Gracias, Nuno; Nicosevici, Tudor; Prados, Ricard; Hurtós, Natàlia; Campos, Ricard; Escartı́n, J.; Elibol, Armağan; Hegedüs, Ramón; Neumann, László

doi:10.1002/9781118868065.ch4

Cited by 17 publications

(12 citation statements)

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“…Images taken in shallow waters (with a depth of less than 10 m) are less affected by water attenuation. However, there are other phenomena that degrade those images, such as flickering [4,38] and higher backscatter due to the presence of suspended particles: just like fog does above water, the floating particles and organisms randomly distributed in the water reflect the projected light back to the sensor and dazzle it [39] (see Figure 1).…”

Section: Challenges Of Underwater Imagingmentioning

confidence: 99%

“…AUVs do not need any pilot nor tether, so their operation vessels can be smaller and less costly. Their diving time is only restricted by the autonomy of the on-board batteries and the required power, so their missions can usually last for several hours [4]. There is a great abundance of marine applications currently carried out by robots, such as underwater archaeology [5,6], ocean monitoring [7,8], marine biology [9] and geology [10,11], damage assessment [12,13] and inspection, maintenance and repair (IMR) applications [14,15], to name a few.…”

Section: Introductionmentioning

confidence: 99%

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State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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Underwater inspection, maintenance and repair (IMR) operations are being increasingly robotized in order to reduce safety issues and costs. These robotic systems rely on vision sensors to perform fundamental tasks, such as navigation and object recognition and manipulation. Especially, active optical 3D scanners are commonly used due to the domain-specific challenges of underwater imaging. This paper presents an exhaustive survey on the state of the art of optical 3D underwater scanners. A literature review on light projection and light-sensing technologies is presented. Moreover, quantitative performance comparisons of underwater 3D scanners present in the literature and commercial products are carried out.

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Section: Challenges Of Underwater Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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show abstract

“…Furthermore, it has also been shown to perform much better than other methods that have been proposed in the literature. Estimated depth using the proposed method can be further used to estimate transmission map and image restoration, using (2) and 1, respectively. Naturally, the improvement in depth map obtained using the proposed method would consequently result in improvement in both transmission map and image restoration.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of hardware approach are using polarisers, remotely operated vehicles and range-gated imaging. Some underwater images are also taken using a stereo camera, with the double lens to [2] IET Image Process. © The Institution of Engineering and Technology 2020 1 capture two images of the objects, from the right and left perspective.…”

Section: Introductionmentioning

confidence: 99%

Depth estimation for underwater images from single view image

Abas

Silva

2020

IET image process

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Underwater images often undergo distortions from scattering, absorption, colour loss, diffraction, polarisation, and varying attenuation depending on the light frequency, due to the water medium. This study answers problems associated with the recovery of underwater images, by developing novel depth map estimation which may be used as an intermediary step for underwater image restoration. The depth map is an important factor for the recovery of the underwater image, as it has been shown that proper estimation of depth, results in better restoration of the underwater image. The proposed algorithm estimates a depth map from a single view image, using blurriness and lighting information, obtained using a simple background neutralisation method. Results from the algorithm on selected raw underwater image data set have been compared with other algorithms and it has been shown that the proposed method gives Pearson coefficients in the range of 0.75-0.95, which are considerably higher and better log root mean square value compared to other methods in the literature. This suggests a better estimation of depth by the proposed method. 10 IET Image Process.

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“…Fig. 1 shows the different distortions in underwater images [1]. When a camera focuses on a target, its lens focuses on the target texture and sends out veiling light, which may be disturbed by particles and marine snow, as well as experiencing Rayleigh, Mie, Back, forward and multiple scatterings.…”

Section: Introductionmentioning

confidence: 99%

Review of underwater image restoration algorithms

Abas

Silva

2019

IET image process

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Underwater images are susceptible to various distortions compared to images taken on land, due to the nature of the water environment. These images often suffer from diffraction, polarisation, absorption, scattering, colour loss and attenuation of light. Each part of the ocean will have its own sources of distortions, due to flickers caused by direct sunlight, marine snow, the fluorescence of biological objects, the presence of macroscopical organisms, loss of stability in divers, loss of light, artificial lighting and floating dust particles present in the water. There are numerous techniques and algorithms that may be used to restore these underwater images. This study reviews different algorithms and methods, developed in the past two decades, to give clearer ideas on the techniques present in the image restoration process, specifically for underwater images.

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Exploring the Seafloor with Underwater Robots

Cited by 17 publications

References 0 publications

State of the Art of Underwater Active Optical 3D Scanners

State of the Art of Underwater Active Optical 3D Scanners

Depth estimation for underwater images from single view image

Review of underwater image restoration algorithms

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