Initial results in underwater single image dehazing

Carlevaris-Bianco, Nicholas; Mohan, Arathi; Eustice, Ryan M.

doi:10.1109/oceans.2010.5664428

Cited by 309 publications

(227 citation statements)

References 17 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

679

414

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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

679

414

View full text Add to dashboard Cite

show abstract

“…Consequently, the dark channel will be estimated by the relation between red and blue/green channels [Carlevaris- Bianco et al 2010]. Other researchers tried neglecting the red channel [Wen et al 2013].…”

Section: Automatic Underwater Image Enhancementmentioning

confidence: 99%

Automatic Underwater Image Enhancement using Improved Dark Channel Prior

Block¹,

Gehmlich²,

Hettmanczyk³

2017

SDH

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Images taken under water are often of a monochromatic appearance, due to the physical interaction (absorption and reflection) between particles and light sources. Enhanced images with improved saturation, for which the monochromatic character has been corrected, are more suitable for generating 3D models and for identifying structures and materials by human experts. In this paper, we present an automatic method to identify the mean water color from a set of images. This mean color represents an average gray and is used to describe a new axis in CIELab color space. An extended color variance and a histogram equalization are simultaneously applied to the image. The main advantage of this method is the fully automatic enhancement process. An UUV (Unmanned Underwater Vehicle) can operate without providing a color reference scheme. The presented method was implemented in the software JEnhancer, which is freely available. JEnhancer was successfully tested in several documentation campaigns, and was integrated into the videogrammetric software pipeline Archaeo3D to produce 3D models from videos.

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“…Preprocessing of underwater images was largely studied and methods have been developed to correct common underwater image artifacts (Bazeille et al 2006;Carlevaris-Bianco et al 2010;Prabhakar & Kumar 2012). However, the resulting image quality is subjective to the observer, and because acquisition conditions can be so different, these methods may not apply well to all datasets.…”

Section: Previous Workmentioning

confidence: 99%

Automated annotation of corals in natural scene images using multiple texture representations

Blanchet

Dery

Landry

et al. 2016

Preprint

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Current coral reef health monitoring programs rely on biodiversity data obtained through the acquisition and annotation of underwater photographs. Manual annotation of these photographs is a necessary step, but has become problematic due to the high volume of images and the high cost of human resources. While automated and reliable multi-spectral annotation methods exist, coral reef images are often limited to visible light, which makes automation difficult. Much of the previous work has focused on popular texture recognition methods, but the results remain unsatisfactory when compared to human performance for the same task. In this work, we present an improved automatic method for coral image annotation that yields consistent accuracy improvements over existing methods. Our method builds on previous work by combining multiple feature representations. We demonstrate that the aggregation of multiple methods outperforms any single method. Furthermore, our proposed system requires virtually no parameter tuning, and supports rejection for improved results. Firstly, the complex texture diversity of corals is handled by combining multiple feature representations: local binary patterns, hue and opponent angle histograms, textons, and deep convolutional activation feature. Secondly, these multiple representations are aggregated using a score-level fusion of multiple support vector machines. Thirdly, rejection can optionally be applied to enhance classification results, and allows efficient semi-supervised image annotation in collaboration with human experts.

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Initial results in underwater single image dehazing

Cited by 309 publications

References 17 publications

Automatic Red-Channel underwater image restoration

Automatic Red-Channel underwater image restoration

Automatic Underwater Image Enhancement using Improved Dark Channel Prior

Automated annotation of corals in natural scene images using multiple texture representations

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