Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

Lu, Huimin; Li, Yujie; Nakashima, Shota; Serikawa, Seiichi

doi:10.1587/transinf.2014edp7405

Cited by 28 publications

(4 citation statements)

References 23 publications

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“…In [21], Guo et al presented a model representing the absorption, scattering, and refraction of water, lenses, and image sensors. In [22], Lu et al proposed a novel underwater imaging model to compensate for the attenuation discrepancy along the propagation path. In [23], Eunpil Park et al proposed a novel underwater image-formation model in which forward scattering was included.…”

Section: Related Work 121 Color Restoration With the Prior Informatio...mentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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As one of the most direct approaches to perceive the world, optical images can provide plenty of useful information for underwater applications. However, underwater images often present color deviation due to the light attenuation in the water, which reduces the efficiency and accuracy in underwater applications. To improve the color reproduction of underwater images, we proposed a method with adjusting the spectral component of the light source and the spectral response of the detector. Then, we built the experimental setup to study the color deviation of underwater images with different lamps and different cameras. The experimental results showed that, a) in terms of light source, the color deviation of an underwater image with warm light LED (Light Emitting Diode) (with the value of Δa*2+Δb*2 being 26.58) was the smallest compared with other lamps, b) in terms of detectors, the color deviation of images with the 3×CMOS RGB camera (a novel underwater camera with three CMOS sensors developed for suppressing the color deviation in our team) (with the value of Δa*2+Δb*2 being 25.25) was the smallest compared with other cameras. The experimental result (i.e., the result of color improvement between different lamps or between different cameras) verified our assumption that the underwater image color could be improved by adjusting the spectral component of the light source and the spectral response of the detector. Differing from the color improvement method with image processing, this color-improvement method was based on hardware, which had advantages, including more image information being retained and less-time being consumed.

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Section: Related Work 121 Color Restoration With the Prior Informatio...mentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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“…As a result, numerous variations of the DCP method have been proposed in the literature. In order to eliminate the problems caused by artificial lighting, Lu et al [23] considered only dual‐channel; red and blue channels, for estimation of the transmission map while using the same fixed water attenuation as in [20] to recover the image. Galdran et al [14] worked with the effect of artificial light using red‐saturation prior, by retrieving the information from the inverse of the red channel.…”

Section: Related Workmentioning

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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“…Lu et al [19] develop a fast weighted guided normalised convolution domain filtering algorithm for restoring underwater images. The algorithm also compensates for light attenuation along the propagation path.…”

Section: Image Restoration Algorithmsmentioning

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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Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

Cited by 28 publications

References 23 publications

The Color Improvement of Underwater Images Based on Light Source and Detector

The Color Improvement of Underwater Images Based on Light Source and Detector

Depth estimation for underwater images from single view image

Review of underwater image restoration algorithms

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