Deriving inherent optical properties from background color and underwater image enhancement

Zhao, Xinwei; Tao, Jin; Song, Qian

doi:10.1016/j.oceaneng.2014.11.036

Cited by 192 publications

(110 citation statements)

References 28 publications

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“…Such underwater images usually are a few color checker images or color image patches taken in the simulated or real underwater environment. For example, Zhao et al [43] treated a plastic color disk as ground truth image and captured its underwater image in a water pool as the testing image. Non-reference Metrics.…”

Section: B Underwater Image Quality Evaluationmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,242

672

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Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 realworld underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the stateof-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj benchmark.html.

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Section: B Underwater Image Quality Evaluationmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,242

672

View full text Add to dashboard Cite

show abstract

“…Previous researches (Trucco & Olmos-Antillon, 2006;Wu & Li, 2013;Zhao, Jin, & Qu, 2015), which used Jaffe's model, presented various types of simplification of the original model. Trucco and Olmos-Antillon (2006) focused on forward scattering and estimated point spread function (PSF) parameters using simplex search.…”

Section: Related Workmentioning

confidence: 99%

“…Wu and Li (2013) also introduced a simplified model and restoring method for forward scattered images. Zhao et al (2015) presented simulation model using Jaffe's model and considering direct attenuation and backscattering components. In this case, forward scattering is ignored.…”

Section: Related Workmentioning

confidence: 99%

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Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Cho

Kim

2018

Journal of Field Robotics

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This paper presents a real‐time and channel‐invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model‐based (extensive) enhancement and a model‐free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real‐world color and grayscale underwater images. Real‐world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB‐SLAM2), a direct (LSD‐SLAM), and a visual underwater SLAM application.

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“…Chiang and Chen 16 restored underwater images by combining a dehazing algorithm 17 with the wavelength compensation algorithm. Zhao et al 21 obtained the inherent optical properties of water from background color. Wen et al 18 developed an underwater imaging model by exploring the difference of light attenuation in between atmosphere and water.…”

Section: Related Workmentioning

confidence: 99%

Single underwater image enhancement based on color cast removal and visibility restoration

Guo

Wang

et al. 2016

J. Electron. Imaging

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Single underwater image enhancement based on color cast removal and visibility restoration,"Abstract. Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods. . His research interests include color image processing, document image analysis, and computational photography. Journal of Electronic Imaging 033012-15 May∕Jun 2016 • Vol. 25(3) Li et al.: Single underwater image enhancement based on color cast removal and visibility restoration Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/17/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Deriving inherent optical properties from background color and underwater image enhancement

Cited by 192 publications

References 28 publications

An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Single underwater image enhancement based on color cast removal and visibility restoration

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