Deep-Sea Organisms Tracking Using Dehazing and Deep Learning

Lu, Huimin; Uemura, Tomoki; Wang, Dong; Zhu, Jihua; Huang, Zi; Kim, Hyoungseop

doi:10.1007/s11036-018-1117-9

Cited by 46 publications

(24 citation statements)

References 19 publications

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“…It should be noted that in (5), except for the coordinate x, all variables are scalars. In other words, the imaging law described in (5) holds in each color channel independently.…”

Section: The Image Formation Modelmentioning

confidence: 99%

“…Since the attenuation coefficient β λ of the red channel is several orders of magnitude greater than that of the blue and green channels under water [39], according to (4), the transmission of the red channel t R will attenuate faster than that of the blue and green channels (t B and t G ) as the scene depth d increases. This will cause the direct component of the red channel of the image to decrease, the backscattering component to increase, and the value of the red channel to drift rapidly to A R , referring to (2), (3), and (5).…”

Section: A the Prior And Validationmentioning

confidence: 99%

“…It has been proved in [3] that the enhancement of underwater image will contribute to a substantial improvement in the performance of underwater optical vision system. Therefore, researchers keep striving to develop different approaches to enhance the original images for a variety of underwater applications, including object recognition [4], rescue missions, human-made structures inspection, ecological monitoring, sea organisms tracking [5], and real-time navigation [6].…”

Section: Introductionmentioning

confidence: 99%

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Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

et al. 2020

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Underwater images suffer from low visibility and contrast caused by absorption and scattering, which leads to haze and some further limitations. The existing underwater single image dehazing methods cannot achieve a balance between the performance and computational complexity, and are difficult to produce satisfactory results in the regions with large distance. To overcome these problems, we propose a new underwater single image dehazing method, which includes an improved background light estimation based on the quad-tree subdivision iteration algorithm, and a novel transmission estimation method. For the background light estimation, we introduce a robust score for each region of the image, which can evaluate the region from both smoothness and color. For the transmission estimation, we propose the color space dimensionality reduction prior (CSDRP), which allows conversing an image from the three-dimensional RGB color space to a 2D color space, namely the UV color space. In the UV color space, by clustering the pixels into mounts of haze-lines and carefully setting the haze-free boundary, the transmission map can be figured out and used to produce an excellent dehazed image. Experimental results show that our method has competitive effects compared with mainstream underwater single image dehazing methods. INDEX TERMS Underwater image dehazing, contrast enhancement, image enhancement, scattering removal.

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“…It should be noted that in (5), except for the coordinate x, all variables are scalars. In other words, the imaging law described in (5) holds in each color channel independently.…”

Section: The Image Formation Modelmentioning

confidence: 99%

Section: A the Prior And Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

et al. 2020

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“…By comparing the Jaffe-McGlamery model and the IFM model, it's very clear that the IFM model is an approximation of the Jaffe-McGlamery model in the case of uniform illumination. In the IFM model, the product of E I (x|λ) and M(x|λ) is replaced by J k (x), and the original form of the backscattering component is replaced by the simplified formula in Equation (5). The forward scattering component and the other parameters are omitted because of their relatively low influence on the value of pixel intensity, and the integral of irradiances of the whole spectrum is simplified by using corresponding parameters of each color channel (i.e., c k and B k ) to keep the solvability of the model.…”

Section: Problem Formulation and Model Improvementmentioning

confidence: 99%

“…As one of the most direct approaches to perceive the world below the surface, optical images can provide plenty of useful information for various underwater applications, such as marine geology surveys, underwater mining, fishery and marine archaeology [1][2][3][4][5]. However, compared with images from terrestrial environment, underwater images often suffer from severe degradation problems, which can impact their reliability and utility in underwater applications.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

Liu

Shang

et al. 2019

Sensors

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In the shallow-water environment, underwater images often present problems like color deviation and low contrast due to light absorption and scattering in the water body, but for deep-sea images, additional problems like uneven brightness and regional color shift can also exist, due to the use of chromatic and inhomogeneous artificial lighting devices. Since the latter situation is rarely studied in the field of underwater image enhancement, we propose a new model to include it in the analysis of underwater image degradation. Based on the theoretical study of the new model, a comprehensive method for enhancing underwater images under different illumination conditions is proposed in this paper. The proposed method is composed of two modules: color-tone correction and fusion-based descattering. In the first module, the regional or full-extent color deviation caused by different types of incident light is corrected via frequency-based color-tone estimation. And in the second module, the residual low contrast and pixel-wise color shift problems are handled by combining the descattering results under the assumption of different states of the image. The proposed method is experimented on laboratory and open-water images of different depths and illumination states. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms many other methods in enhancing the quality of different types of underwater images, and is especially effective in improving the color accuracy and information content in badly-illuminated regions of underwater images with non-uniform illumination, such as deep-sea images.

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Learning spectral normalized adversarial systems with stacked structure for high‐quality 3D object generation

Zhang

Qiu

Wang

et al. 2019

Concurrency and Computation

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Summary This paper proposes a new method for generating 3D objects based on generative adversarial networks (GANs). Recently, GANs have been used in 3D object generation, but it is still very challenging to generate high‐quality 3D objects because of the complex data distribution over 3D objects. In this paper, we propose a system based on GAN that makes the generated objects more realistic. We use multiple generators and discriminators to enhance the ability of the model for learning complex distributions. Such a stacked structure can be considered as a coarse‐to‐fine or low‐to‐high–resolution mechanism. We employ the spectral normalization technology to control the Lipschitz constant of the discriminators by literally constraining the spectral norm of each layer to get a more stable training process. In this way, the proposed model can generate realistic and high‐quality 3D objects. Moreover, our system can also recover incomplete 3D objects into complete 3D objects. Experiments demonstrate that our model performs better in the quality of the generated objects than the baselines.

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Deep-Sea Organisms Tracking Using Dehazing and Deep Learning

Cited by 46 publications

References 19 publications

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

Learning spectral normalized adversarial systems with stacked structure for high‐quality 3D object generation

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