Application of underwater hyperspectral data for color correction purposes

Åhlén, Julia; Sundgren, David; Bengtsson, Ewert

doi:10.1134/s105466180701021x

Cited by 30 publications

(10 citation statements)

References 2 publications

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“…3(b) for camera II. They have been respectively deduced from spectral responses given in [28] and [29], and slightly modified in order to have simple shapes, their maxima at the same RGB wavelengths, and the same energy in each channel (i.e., P n ϕ c λ n Δλ 1, c ∈ fR; G; Bg).…”

Section: B Rgb Color Image Simulationmentioning

confidence: 99%

“…To evaluate quantitatively the quality of the image simulation, the error ϵ c • N between the ground truth image E c •;true and the image simulated with N wavelengths E c •;N is determined by averaging their absolute difference over the whole pixels of the image: They were adapted (a) from [28] for camera I and (b) from [29] for camera II.…”

Section: Influence Of the Spectral Discretizationmentioning

confidence: 99%

“…Studying color degradation impact on the image quality and on the performance of image processing techniques, such as color restoration algorithms [7,[15][16][17][18][19], is thus an important field of underwater image processing. For this purpose, it is therefore crucial to simulate images with a color rendering as realistic as possible.…”

Section: Introductionmentioning

confidence: 99%

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Color image simulation for underwater optics

2012

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Underwater optical image simulation is a valuable tool for oceanic science, especially for the characterization of image processing techniques such as color restoration. In this context, simulating images with a correct color rendering is crucial. This paper presents an extension of existing image simulation models to RGB imaging. The influence of the spectral discretization of the model parameters on the color rendering of the simulated images is studied. It is especially shown that, if only RGB data of the scene chosen for simulations are available, a spectral reconstruction step prior to the simulations improves the image color rendering.

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Section: B Rgb Color Image Simulationmentioning

confidence: 99%

Section: Influence Of the Spectral Discretizationmentioning

confidence: 99%

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Color image simulation for underwater optics

2012

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“…Chambah et al 12 developed a color correction method based on an automatic color equalization model, 13 wherein the method is unsupervised, robust, and has local filtering properties leading to more effective results. Ahlen et al 21 presented a method to estimate a hyperspectral image from an RGB image and pointwise hyperspectral data. Torres-Méndez and Dudek 14 used a Markov random field to represent the relationship between a color-depleted image and a corresponding color image.…”

Section: Introductionmentioning

confidence: 99%

Underwater image enhancement by dehazing and color correction

Guo

2015

J. Electron. Imaging

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Poor visibility due to the effects of light absorption and scattering is challenging for processing underwater images. We propose an approach based on dehazing and color correction algorithms for underwater image enhancement. First, a simple dehazing algorithm is applied to remove the effects of haze in the underwater image. Second, color compensation, histogram equalization, saturation, and intensity stretching are used to improve contrast, brightness, color, and visibility of the underwater image. Furthermore, bilateral filtering is utilized to address the problem of the noise caused by the physical properties of the medium and the histogram equalization algorithm. In order to evaluate the performance of the proposed approach, we compared our results with six existing methods using the subjective technique, objective technique, and color cast tests. The results show that the proposed approach outperforms the six existing methods. The enhanced images, as a result of implementing the proposed approach, are characterized by relatively genuine color, increased contrast and brightness, reduced noise level, and better visibility. Li, and Guo: Underwater image enhancement by dehazing and color correction Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/03/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 033023-3 May∕Jun 2015 • Vol. 24(3) Li, and Guo: Underwater image enhancement by dehazing and color correction Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/03/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 033023-5 May∕Jun 2015 • Vol. 24(3) Li, and Guo: Underwater image enhancement by dehazing and color correction Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 07/03/2015 Terms of Use: http://spiedl.org/terms underwater images (single fish, multiple fish_a, and multiple_fish_b), (b) Gray World (GW)'s results, (c) white balance (WB)'s results, (d) automatic white balance (AWB)'s results, (e) white patch Retinex (WPR)'s results, (f) McCann Retinex (MCR)'s results, (g) contrast-limited adapt histogram equalization (CLAHE)'s results, and (h) results of our proposed approach.

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“…In this section, we show the interest of underwater image simulation to study color image restoration algorithms. Indeed, color degradation and restoration are major aspects of underwater imaging and are the subject of various studies [4], [12], [13], [14], [15].…”

Section: A Valuable Tool For Image Processingmentioning

confidence: 99%

Phenomenological marine snow model for optical underwater image simulation: Applications to color restoration

Boffety

Galland

2012

2012 Oceans - Yeosu

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To cite this version:Abstract-Optical imaging plays an important role in oceanic science and engineering. However, the design of optical systems and image processing techniques for subsea environment are challenging tasks due to water turbidity. Marine snow is notably a major source of image degradation as it creates white bright spots that may strongly impact the performance of image processing methods. In this context, it is necessary to have a tool to foresee the behavior of these methods in marine conditions. This paper presents a phenomenological model of marine snow for image simulation. In order to highlight the interest of such a modeling for image processing characterization, the impact of marine snow perturbation on a color restoration technique is analyzed and a solution to improve the robustness of the algorithm is finally proposed.

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Application of underwater hyperspectral data for color correction purposes

Cited by 30 publications

References 2 publications

Color image simulation for underwater optics

Color image simulation for underwater optics

Underwater image enhancement by dehazing and color correction

Phenomenological marine snow model for optical underwater image simulation: Applications to color restoration

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