Underwater image dehazing using joint trilateral filter

Serikawa, Seiichi; Lu, Huimin

doi:10.1016/j.compeleceng.2013.10.016

Cited by 395 publications

(118 citation statements)

References 18 publications

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“…Therein, we realize that (25) is the method that recovers the least vivid colors. On the other hand, the methods in (18), (19) and (26) are obtaining very saturated colors, but clearly sacrificing the reduction of color dominance, and to some extent, also color cast. We thus see that, among the three methods that are able to reduce color dominance and color cast, Ancuti's method (9) and ours are again ranked as the first/second with respect to the ability to retrieve intense colors.…”

Section: Color Correction Evaluationmentioning

confidence: 99%

Automatic Red-Channel underwater image restoration

Galdrán

Pardo

Picón³

et al. 2015

Journal of Visual Communication and Image Representation

767

420

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Underwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.

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Section: Color Correction Evaluationmentioning

confidence: 99%

Automatic Red-Channel underwater image restoration

Galdrán

Pardo

Picón³

et al. 2015

Journal of Visual Communication and Image Representation

767

420

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show abstract

“…Fig. 2 and 3 shows the input images and corresponding results of the underwater image haze removal by Joint Trilateral Filter [24]. It is clearly shown that all the haze has been removed to a great extent but image look dark after haze removal.…”

Section: Eperimental Results and Discussionmentioning

confidence: 99%

“…It successfully removed the haze from underwater images to a great extent. In this section, first of all, we compare the existing technique joint trilateral filter [24] with our proposed technique fuzzy based DCP and AHE for underwater image haze removal by showing their comparison through images. Then both techniques are compared on the basis of values evaluated by various image performance metrics through tables and graphs.…”

Section: Eperimental Results and Discussionmentioning

confidence: 99%

“…Backscattering result in the loss of contrast between object and its surroundings generating a characteristic veil that superimposes itself on the image and hides the scene [11]. Color change corresponds to varying degrees of attenuation encountered by light travelling in water with different wavelengths rendering water dominated by a bluish tone [24]. But the biggest problem in underwater images is haze.…”

Section: Introductionmentioning

confidence: 99%

“…The underwater images have problems such as limited range of visibility, low contrast, non-uniform lighting and illumination, bright artifacts, noise, blurring, and diminishing color. These are caused by factors such as: Scattering is caused by large suspended particles such as turbid water [24] which result in blurring of image features. Backscattering result in the loss of contrast between object and its surroundings generating a characteristic veil that superimposes itself on the image and hides the scene [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

Talwar¹,

Kochher²

2015

IJCA

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Underwater images are used to explore the unique life and the world that exists under the water. These images have less clarity, diminishing colors, low contrast .All these issues are a result of haze in underwater images. So, Underwater image haze removal algorithms become very important and necessary for many vision applications. In this paper, after a brief overview of existing methods of haze removal and discovering their limitations, we present a novel technique of underwater image haze removal based on Fuzzy Based DCP and AHE. AHE has been used to remove the problem of uneven illumination. The proposed technique has the ability to remove the limitations of existing techniques. Different kind of image quality assessment metrics have been used to evaluate the effectiveness of proposed technique over the existing one. The proposed technique give efficient results for different hazy underwater images by removing the haze to a good extent and improving the quality.

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References

2017

Computer Vision in Vehicle Technology

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Underwater image dehazing using joint trilateral filter

Cited by 395 publications

References 18 publications

Automatic Red-Channel underwater image restoration

Automatic Red-Channel underwater image restoration

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

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