Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus, to address these challenges, several studies have been conducted to investigate deep learning techniques; however, they mostly focused on specific learning approaches, such as supervised deep learning. In addition, these studies did not comprehensively investigate other deep learning techniques, such as deep unsupervised and deep reinforcement learning techniques. Moreover, the majority of these studies neglect to discuss some main methodologies in deep learning, such as transfer learning, federated learning, and online learning. Therefore, motivated by the limitations of the existing studies, this study summarizes the deep learning techniques into supervised, unsupervised, reinforcement, and hybrid learning-based models. In addition to address each category, a brief description of these categories and their models is provided. Some of the critical topics in deep learning, namely, transfer, federated, and online learning models, are explored and discussed in detail. Finally, challenges and future directions are outlined to provide wider outlooks for future researchers. 

pdf/deep-learning-systematic-review-models-challenges-and-3olctoztrj.pdf

Deep learning: systematic review, models, challenges, and research directions

Different underwater images captured by different devices may exhibit varying degrees of nonlinear rendering, leading to white balance distortion. Meanwhile, wavelength attenuation and light scattering associated with distance can cause color shifts, reduced visibility, and decreased contrast in underwater images. This will result in difficult access to underwater information, which in turn will affect the application of advanced vision. Considering these degradation issues, we propose a hybrid fusion method for underwater image enhancement, called HFM. In terms of technical contributions, we introduce a color and white balance correction module that addresses color and white balance distortion in underwater images using the gray world principle and a nonlinear color mapping function. We design a visibility recovery module based on type-II fuzzy sets and a contrast enhancement module using curve transformation. Besides, inspired by image fusion methods, we propose an underwater image perception fusion module that focuses on two different tasks simultaneously, fusing underwater images of visibility and contrast. Therefore, the proposed method can effectively solve the problems of white balance distortion, color shift, low visibility and low contrast in underwater images, and achieves optimal results in application tests of geometric rotation estimation, feature point matching and edge detection. Through comparative experiments analyzed on four real scene datasets, the proposed method achieves superior results compared to 14 state-of-the-art underwater image enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/HFM. 

HFM: A hybrid fusion method for underwater image enhancement

This paper delves into enhancing underwater images to improve human observation of underwater scenes. To this end, a reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes is proposed, referred to as INSPIRATION. This paradigm is so named because it constructs rewards for guiding model training by incorporating metrics inspired by human visual perception, eliminating the need for reference images. It overcomes the limitations of deep models, which often result in poor performance in dynamic and diverse underwater scenes due to the scarcity of underwater images with enhancement references. Specifically, a residual-enhancement network, composed of residual modules and a channel-attention module, is utilized to extract features as a state. An extensible collection encompassing diverse image enhancement algorithms is utilized to provide an image enhancement algorithm as an action. A multi-non-reference human visual perception metric increment is utilized as a reward, and proximal policy optimization (PPO) is utilized to conduct reinforcement learning. This paradigm treats underwater images as sole inputs during both training and implementation, learning and organizing a sequence of image enhancement algorithms to explicitly achieve a step-wise image enhancement process, aligning the enhanced images with human visual perception. Extensive qualitative and quantitative evaluations conclusively demonstrate that our paradigm outperforms nine state-of-the-art underwater image enhancement methods in terms of visual quality and achieves better performance on five underwater image quality assessment measures across three underwater image datasets. Additionally, the potential advantages of our paradigm as a pre-processing step in other underwater computer vision applications are demonstrated. The code has been released publicly for reproducibility and evaluations at https://gitee.com/wanghaoupc/uie_inspiration. 

INSPIRATION: A reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes

Accurate prediction of motion dynamics fundamentally promotes the autonomy of intelligent ships, but faces great challenges in modeling mechanism. In this paper, to establish data-driven recurrent mapping within ship motion dynamics, an ultrashort-term deep learning predictor is innovatively developed by elaborately creating a self-attention-weighted bidirectional long short-term memory (Bi-LSTM) network in conjunction with 1-dimensional convolution (Conv-1D), named SeaBil. To be specific, combined with the sliding-window technique, the Conv-1D is devised to convert 5-dimensional-input samples consisting of course angle, yaw rate, roll angle, total speed and rudder angle into 1-D-feature vectors, thereby extracting coupled feature maps within the current data-window. The Bi-LSTM is further deployed to recurrently learn forward and reverse feature maps of ship motion time-series data. Self-attention mechanism cascaded in the serial is employed to adaptively assign time-varying weights of sample instants within the current window such that the ultrashort-term prediction of course angle, yaw rate, roll angle and total speed can be achieved. Eventually, comprehensive comparisons to typical approaches using real-world samples demonstrate the superiority of SeaBil in terms of ultrashort-term prediction accuracy. 

SeaBil: Self-attention-weighted ultrashort-term deep learning prediction of ship maneuvering motion

Semantic attention and relative scene depth-guided network for underwater image enhancement

Deep learning-based visual detection of marine organisms: A survey

Coordinate Attention and Transformer Neck-Based Marine Organism Detection

In this paper, to effectively strengthen quality of underwater image enhancement from both channel and spatial viewpoints, an adaptive channel attention-based deformable generative adversarial networks (ACADGAN) framework is established. Main contributions are as follows. 1) By virtue of multi-branch convolution architecture with dilated convolution mechanism, the adaptive channel attention (ACA) is devised, such that channel weight can be adaptively recalibrated, and thereby significantly contributing to preserving content features from channel viewpoint. 2) By augmenting offset position of sampling point with respect to convolution kernel, the deformable convolution network (DCN) is created, such that detailed information of underwater image can be dramatically retained from spatial aspect. 3) The ACADGAN scheme is eventually proposed by integrating ACA and DCN modules with a deep generative adversarial network. Comprehensive experiments demonstrate the remarkable effectiveness and superiority of the developed ACADGAN scheme. 

Adaptive Channel Attention-Based Deformable Generative Adversarial Network for Underwater Image Enhancement

In this article, to exclusively suppress unuseful underwater noise feature and effectively avoid overenhancement, simultaneously, an underwater attentional generative adversarial network (UAGAN) is innovatively established. Main contributions are as follows: combining dense concatenation with global maximum and average pooling techniques, a cascade dense-channel attention (CDCA) module is devised to adaptively distinguish noise feature and recalibrate channel weight, simultaneously, such that low-contribution feature map can be effectively suppressed; to sufficiently capture long-range dependence between any two nonlocal spatial patches, the position attention (PA) module is created such that the deviation among independent patches can be sufficiently eliminated, thereby avoiding overenhancement; and in conjunction with CDCA and PA modules, the entire UAGAN framework is eventually developed in an end-to-end manner. Comprehensive experiments conducted on underwater image enhancement benchmark (UIEB) and underwater robot professional contest (URPC) datasets demonstrate remarkable effectiveness and superiority of the proposed UAGAN scheme by comparing with typical underwater image enhancement approaches including unsupervised color correction method, image blurriness and light absorption, underwater dark channel prior, underwater generative adversarial network, underwater convolutional neural network, and WaterNet in terms of peak signal-to-noise ratio, underwater color image quality evaluation, underwater image quality measures, etc.

Underwater Attentional Generative Adversarial Networks for Image Enhancement

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