The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Bayesian retinex underwater image enhancement

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024&#x00D7;1024&#x00D7;3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html. 

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

The Retinex models the human visual system to perceive natural colors, which could improve the contrast and sharpness of the degraded image and also provide color constancy and dynamic range simultaneously. This endows the Retinex exceeding advantages for enhancing the underwater image. Based on the multi-scale Retinex, an efficient enhancement method for underwater image and video is presented in this paper. Firstly, the image is pre-corrected to equalize the pixel distribution and reduce the dominating color. Then, the classical multi-scale Retinex with intensity channel is applied to the pre-corrected images for further improving the contrast and the color. In addition, multi-down-sampling and infinite impulse response Gaussian filtering are adopted to increase processing speed. Subsequently, the image is restored from logarithmic domain and the illumination of the restored image is compensated based on statistical properties. Finally, the color is selectively preserved by the inverted gray world method depending on imaging conditions and application requirements. Five kinds of typical underwater images with green, blue, turbid, dark and colorful backgrounds and two underwater videos are enhanced and evaluated on Jetson TX2, respectively, to verify the effectiveness of the proposed method.

Efficient underwater image and video enhancement based on Retinex

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different data sets and different metrics. In this paper, we set up an effective and pubic underwater test dataset named U45 including the color casts, low contrast and haze-like effects of underwater degradation and propose a fusion adversarial network for enhancing underwater images. Meanwhile, the well-designed the adversarial loss including Lgt loss and Lfe loss is presented to focus on image features of ground truth, and image features of the image enhanced by fusion enhance method, respectively. The proposed network corrects color casts effectively and owns faster testing time with fewer parameters. Experiment results on U45 dataset demonstrate that the proposed method achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, an ablation study demonstrates the contributions of each component, and the application test further shows the effectiveness of the enhanced images.

A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset.

An imaging-inspired no-reference underwater color image quality assessment metric

In underwater electro-optic detection, image quality can be degraded by the backscattering of light from the illuminated water volume. In practical systems, we tend to simultaneously require a high level of detection distance (DD), field of view (FOV), and depth of field (DOF), but these factors influence each other by the media scattering. To eliminate this restriction, we propose to explore the underwater wide-area layered light field (UWLLF), which classifies the underwater detection area by the DD and distribution characteristics of the light field, to minimize the scattering influence on target detection. Based on the UWLLF, an underwater electro-optic detection system is designed that can achieve the specifications of a 70° FOV and 7.9-fold attenuation length (for the attenuation coefficient 1.43 /m of 532 nm) DD. In addition, with the spatial separation of light energy, the non-detection zone at short ranges is eliminated, yielding an almost full DOF. With these three factors simultaneously improved, the ability of underwater exploration for object detection is enhanced.

Underwater Wide-Area Layered Light Field for Underwater Detection

Compared with the atmospheric imaging, in underwater electro-optic imaging (UEOI), there exist difficulties to describe the target discrimination level within the visibility range, because of the media scattering and inhomogeneous illumination. The large-scale contrast is used to describe the target discrimination traditionally, while it is not linear to human perception. In this paper, the detectability of a UEOI system is firstly defined to quantify the perceptive target discrimination level in full space. And we present a target detectability assessment metric (TDAM), which is used to illustrate the detectability distribution of a UEOI system. Additionally based on the large-scale contrast, an automatic detectability calculation pipeline is proposed, and a trapper-shaped function fits the contrast and detectability well, with the Spearman rank order correlation coefficient (SROCC = 0.8852), Pearson linear correlation coefficient (PLCC = 0.8965) and root-mean-square error (RMSE = 1.6532).

A target detectability assessment metric for the underwater electro-optic imaging system

The complex seawater environment will cause the absorption and scattering of optical signals, and the light will be seriously attenuated within several hundred meters of transmission in seawater. Therefore, the development of underwater wireless optical communication (UWOC) has great limitations. Orthogonal frequency division multiplexing (OFDM) is suitable for promoting the development of optical communication in an underwater environment with the characteristics of high bandwidth and data rate. We propose and experimentally demonstrate an underwater wireless optical communication system employing 16-quadrature amplitude modulation OFDM (16QAM-OFDM) and cost-effective TO56 packaged blue-light laser diode (LD). Over a 10m tap water channel, we have achieved a 125Mbps transmission and the bit error rate (BER) is 1.71 × 10−3, which is lower than the forward error correction (FEC) criterion.

Mengnan Sun

Papers

An imaging-inspired no-reference underwater color image quality assessment metric

Underwater Wide-Area Layered Light Field for Underwater Detection

A target detectability assessment metric for the underwater electro-optic imaging system

A design of Underwater Wireless Optical Communication system based on OFDM technology