Underwater Depth Estimation and Image Restoration Based on Single Images

Drews, Paul; Nascimento, Erickson R.; Botelho, Silvia Silva da Costa

doi:10.1109/mcg.2016.26

Cited by 491 publications

(303 citation statements)

References 9 publications

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“…In [36], DCP was combined with the wavelength-dependent compensation algorithm to restore underwater images. In [37], an Underwater Dark Channel Prior (UDCP) was proposed based on the fact that the information of the red channel in an underwater image is undependable. Based on the observation that the dark channel of the underwater image tends to be a zero map, Liu and Chau [38] formulated a cost function and minimized it so as to find the optimal transmission map, which is able to maximize the image contrast.…”

Section: A Underwater Image Enhancement Methodsmentioning

confidence: 99%

“…With the candidate underwater images, the potential reference images are generated by 12 image enhancement methods, including 9 underwater image enhancement methods (i.e., fusion-based [31], two-step-based [32], retinex-based [33], UDCP [37], regression-based [39], GDCP [40], Red Channel [42], histogram prior [45], and blurriness-based [46]), 2 image dehazing methods (i.e., DCP [35] and MSCNN [66]), and 1 commercial application for enhancing underwater images (i.e., dive+ 8 ). We exclude the recent deep learning-based methods due to their limited generalization capability to the diverse real-world underwater images and the fixed size of network output [53], [55].…”

Section: B Reference Image Generationmentioning

confidence: 99%

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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: A Underwater Image Enhancement Methodsmentioning

confidence: 99%

Section: B Reference Image Generationmentioning

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

“…We compare our depth estimation method with other underwater depth estimation methods by Drews et al (UDCP) [5], Peng et al [7] and Berman et al [9]. We also compare with a deep learning based depth estimation method by Godard et al For the fine-tuned model, we re-trained the KITTI model for 20 epochs on 2000 stereo pairs randomly picked from three different subsets of CADDY dataset [25].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Single underwater image depth estimation approaches [5,6,7,8,9] mainly focus on image restoration. They estimate the transmission-map as an intermediate step and derive depth-maps and restored images from that.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches produce good results but due to the possibility of multiple depth-maps from a single transmissionmap, a better approach would be to directly estimate depth from the underwater image. Berman et al [9] Drews et al [5] UW-Net (ours) Input Fig. 1: Instead of following the usual approach of obtaining depthmap from transmission-map, we propose a novel depth estimation network based on unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Single Image Underwater Depth Estimation

Gupta

Mitra

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken "in the wild" by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.

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A novel underwater image restoration method based on decomposition network and physical imaging model

Cui

Sun

Jian

et al. 2021

Int J of Intelligent Sys

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Underwater image restoration is one of the significant research in marine engineering and aquatic robotics. However, due to the propagation characteristics of light and the serious turbidity in underwater, the captured images often have chromatic aberration and scattering blur, which brings great challenges to the restoration of the raw image. In this paper, a revised underwater imaging model is proposed first, which reanalyzes the generation of background light from the atmosphere to the underwater and provides important support for underwater color correction. And then a network framework via the revised model is designed, which can decompose the captured image into different components corresponding to the revised model. The proposed network consists of a decomposition architecture with residual blocks that learns a complete separation of clear image and transmittance features. These two features are used along with the raw image to predict the background light. Finally, combining three constraints of the imaging model, the proposed framework can converge rapidly along the desired direction. By comparison with the performance of the state‐of‐the‐art algorithms, the designed network shows excellent visibility and is capable of removing water on both synthetic and real‐world images in different water types.

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Underwater Depth Estimation and Image Restoration Based on Single Images

Cited by 491 publications

References 9 publications

An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Unsupervised Single Image Underwater Depth Estimation

A novel underwater image restoration method based on decomposition network and physical imaging model

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