SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions. Accordingly, we propose semantic region-wise enhancement module to perceive the degradation of different semantic regions from multiple scales and feed it back to the global attention features extracted from its original scale. This strategy helps to achieve robust and visually pleasant enhancements to different semantic objects, which should thanks to the guidance of semantic information for differentiated enhancement. More importantly, for those degradation types that are not common in the training sample distribution, the guidance connects them with the already well-learned types according to their semantic relevance. Extensive experiments on the publicly available datasets and our proposed dataset demonstrated the impressive performance of SGUIE-Net. The code and proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.htm

Global oceanic mesoscale eddies trajectories prediction with knowledge-fused neural network

Efficient eddy trajectory prediction driven by multiinformation fusion can facilitate the scientific research of oceanography, while the complicated dynamics mechanism makes this issue challenging. Benefiting from ocean observing technology, the eddy trajectory dataset can be qualified for data-intensive research paradigms. In this article, the dynamics mechanism is used to inspire the design idea of the eddy trajectory prediction neural network (termed EddyTPNet) and is also transformed into prior knowledge to guide the learning process. This study is among the first to implement eddy trajectory prediction with physics informed neural network. First, an in-depth analysis of the kinematic characteristics indicates that the longitude and latitude of the trajectory should be decoupled; second, the directional dispersion prior knowledge of global eddy propagation is embedded into the decoder of the EddyTPNet to improve the performance; finally, EddyTPNet predicts global eddy trajectories through pretraining and adapts to complex local regions via model transfer. Extensive experimental results demonstrate that EddyTPNet can reliably forecast the motion of eddies for the next seven days, ensuring a low daily mean geodetic error. This exploratory study provides valuable insights into solving the prediction problem of ocean phenomena by using knowledge-based time-series neural networks

Single image dehazing and denoising combining dark channel prior and variational models

Single image dehazing and denoising models can simultaneously remove haze and noise with high efficiency. Here, the authors propose three variational models combining the celebrated dark channel prior (DCP) and total variations (TV) models for image dehazing and denoising. The authors firstly estimate the transmission map associated with depth using DCP, then design three variational models for colour image dehazing and denoising based on this estimation and the layered total variation (LTV) regulariser, multichannel total variation (MTV) regulariser, and colour total variation (CTV) regulariser, respectively. In order to improve the computation efficiency of the three models, the authors design their fast split Bregman algorithms via introducing some auxiliary variables and the Bregman iterative parameters. Numerous experiments are presented to compare their denoising effects, edge‐preserving properties, and computation efficiencies. To demonstrate the merits of the proposed models, the authors also conduct some comparisons with several existing state‐of‐the‐art methods. Numerical results further prove that the LTV‐based model is fastest, and the CTV model is the best for denoising with edge‐preserving, and it also leads to the best visually haze‐free and noise‐free images

WildFishNet: Open Set Wild Fish Recognition Deep Neural Network With Fusion Activation Pattern

Wild fish recognition is a fundamental problem of ocean ecology research and contributes to the understanding of biodiversity. Given the huge number of wild fish species and unrecognized category, the essence of the problem is an open set fine-grained recognition. Moreover, the unrestricted marine environment makes the problem even more challenging. Deep learning has been demonstrated as a powerful paradigm in image classification tasks. In this article, the wild fish recognition deep neural network (termed WildFishNet) is proposed. Specifically, an open set fine-grained recognition neural network with a fused activation pattern is constructed to implement wild fish recognition. First, three different reciprocal inverted residual structural modules are combined by neural structure search to obtain the best feature extraction performance for fine-grained recognition; next, a new fusion activation pattern of softmax and openmax functions is designed to improve the recognition ability of open set. Then, the experiments are implemented on the WildFish dataset that consists of 54 459 unconstrained images, which includes 685 known classes and 1 open set unrecognized category. Finally, the experimental results are analyzed comprehensively to demonstrate the effectiveness of the proposed method. The in-depth study also shows that artificial intelligence can empower marine ecosystem research

Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network

Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain

Variational total curvature model for multiplicative noise removal

The multiplicative noise removal problem has received considerable attention recently. To solve this problem, various variational models have been proposed, which minimise an energy functional composed of the data term and the regularisation term. Regarding the regularisation term, a first‐order model is frequently used to remove multiplicative noise, which may cause staircase effect and loss of contrast in the output image. In this study, the authors use a second‐order model, the total curvature (TC), to solve the above problem. The TC model has the benefit of removing the staircase effect and maintaining image edges, contrasts and corners. The augmented Lagrange method is utilised to solve the proposed TC model by introducing auxiliary variables, Lagrange multipliers and using alternating optimisation strategy. In each loop of optimisation, the fast Fourier transform, generalised soft threshold formulas, projection method and gradient descent method are integrated effectively. The experimental results show that the TC model can effectively remove staircase effect and preserve smoothness, via comparison with the first‐order model (total variation regularisation and Perona–Malik regularisation). Furthermore, the TC model is better than another second‐order model based on bounded Hessian regularisation in preserving contrast and corner