• Propose a multimodal-inspired underwater video enhancement framework. • Introduce collision-aware noise injection to simulate feature-level conflicts. • Design a hybrid ViT-convolutional architecture with low computational cost. • Achieve improved clarity and temporal consistency across underwater frames. Underwater video enhancement addresses degradation from absorption, scattering, and turbidity, which hinders visual tasks such as detection and tracking. Unlike traditional single-frame methods, we treat temporal cues, occlusion patterns, and structured degradations as implicit modalities within video streams. To this end, we propose CAECNet (Collision-Attack Error Correction Network), a novel multimodal-inspired underwater video enhancement network that integrates the ‘Collision Attack’ training strategy with the ‘Error Correction’ mechanism. This network overcomes the limitations of traditional single-frame enhancement methods, implementing high-precision and real-time inference. It improves temporal perception through multi-frame fusion and utilizes previous frames to assist real-time inference, meeting the demands of dynamic processing. By incorporating a Vision Transformer (ViT) and a lightweight depthwise separable convolution module, the network enhances spatial feature representation and computational efficiency. A branching-based error correction upsampler is designed to correct feature representation errors and reduce information entropy loss, thereby improving video detail restoration quality. The “Collision Attack” training strategy injects structured noise to accelerate network feature learning and reduce computational costs. Experimental results show that CAECNet significantly outperforms existing methods on multiple underwater video datasets, improving image clarity, inter-frame consistency, and computational efficiency, making it suitable for underwater robotic intelligent perception tasks.

Underwater video enhancement is crucial for marine research and monitoring applications, particularly in the context of the Internet of Things (IoT), where autonomous underwater vehicles (AUVs) and sensor networks are deployed for environmental monitoring and tracking marine life. However, the scarcity of undistorted underwater video data and distortions, such as motion blur and water turbidity, limit the effectiveness of enhancement models. Existing methods typically focus on frame-by-frame enhancement and overlook temporal coherence and computational efficiency. To address these issues, we propose SGUVE-Net (Semantic-Guided Underwater Video Enhancement Network), which combines a multi-scale feature-aware network with a semantic branch for localized enhancement. The main branch employs an encoder-decoder architecture, combining spatial group shifting and dual attention mechanisms to fully exploit contextual information for precise alignment. In contrast, the semantic branch focuses on enhancing key regions of the video frames by incorporating high-level semantic cues, which improves motion tracking accuracy and mitigates motion blur, thereby enhancing video quality for real-time IoT applications. They complement each other to achieve differentiated modeling of static background details and dynamic target features. Experimental results show that SGUVE-Net outperforms state-of-the-art methods across several metrics, providing an effective solution for underwater video enhancement in IoT systems.

Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. Existing enhancement methods partially address these issues, but fail to effectively decouple nonlinear relationships among degradation factors, leading to inconsistent performance. To address these challenges, we propose a degradation-content decoupling-based underwater image enhancement network (DCDN). The framework integrates a super-fusion cascade module for dynamic feature weighting, reducing artifacts, and combines multichannel color space transformation with texture-guided correction to decouple and optimize degradation factors. This approach improves color fidelity and texture detail restoration by refining color information and adapting local textures. Experiments on public datasets demonstrate that DCDN outperforms existing methods in various underwater scenarios. This work enhances the visual capabilities of underwater robots, supporting intelligent transportation applications, such as marine logistics and underwater inspections.

Feature drift is caused by the dynamic coupling of target features and degradation factors, which reduce underwater detector performance. We redefine feature drift as the instability of target features within boundary constraints while solving partial differential equations (PDEs). From this insight, we propose the Spatial Residual (SR) block, which uses SkipCut to establish effective constraints across the network width for solving PDEs and optimizes the solution space. It is implemented as a general-purpose backbone with 5 Spatial Residuals (BSR5) for complex feature scenarios. Specifically, BSR5 extracts discrete channel slices through SkipCut, where each sliced feature is parsed within the appropriate data capacity. In gradient backpropagation, SkipCut functions as a ShortCut, optimizing information flow and gradient allocation to enhance performance and accelerate training. Experiments on the RUOD dataset show that BSR5-integrated DETRs and YOLOs achieve state-of-the-art results for conventional and end-to-end detectors. Specifically, our BSR5-DETR improves 1.3% and 2.7% AP than RT-DETR with ResNet-101, while reducing parameters by 41.6% and 6.6%, respectively. Further validation highlights BSR5's strong convergence and robustness, especially in training from scratch scenarios, making it well suited for data-scarce, resource-constrained, and real-time tasks.

Underwater imaging is essential for marine remote sensing tasks, such as environmental monitoring, resource exploration, and autonomous navigation. However, images captured in underwater environments often suffer from complex degradations, including wavelength-dependent color distortion, contrast attenuation, and structural detail loss. To address these challenges, we propose a Cross-Scale Style-Guided Network (CSG-Net) for robust underwater image enhancement. CSG-Net employs a dual-stage collaborative framework that decouples global degradation modeling from local detail refinement. In the first stage, a Style Extraction Network (SE-Net) extracts multi-scale degradation-aware style priors that implicitly encode large-scale physical degradation patterns, such as red-channel attenuation and spectral imbalance. In the second stage, a Style-Guided Enhancement Network (SG-Net) leverages these style features to guide spatially adaptive enhancement, enabling consistent color correction and fine-grained detail recovery. To alleviate semantic degradation during scale transitions, CSG-Net introduces the proposed multi-resolution feature-preserving cross-scale interaction (MFPCSI) module, which enhances the preservation and integration of hierarchical features. Combined with the Multi-Stream Information Fusion (MSIF) module, this design enables the effective fusion of semantic and structural information across spatial scales. The proposed components enable the preservation of fine-grained details while adaptively integrating semantic and structural cues across multiple scales. Comprehensive experiments conducted on diverse and challenging underwater image datasets demonstrate that CSG-Net consistently surpasses state-of-the-art approaches in terms of PSNR, SSIM, and UIQM. Furthermore, the model exhibits strong cross-domain generalization and delivers high-fidelity visual results, underscoring its suitability for deployment in practical vision systems operating under complex, real-world environments.

Underwater data is inherently scarce and exhibits complex distributions, making it challenging to train high-performance models from scratch. In contrast, in-air models are structurally mature, resource-rich, and offer strong potential for transfer. However, significant discrepancies in visual characteristics and feature distributions between underwater and in-air environments often lead to severe performance degradation when applying in-air models directly. To address this issue, we propose IA2U, a lightweight plugin designed for efficient underwater adaptation without modifying the original model architecture. IA2U can be flexibly integrated into arbitrary in-air networks, offering high generalizability and low deployment costs. Specifically, IA2U incorporates three types of prior knowledge-water type, degradation pattern, and sample semantics-which are embedded into intermediate layers through feature injection and channel-wise modulation to guide the network's response to underwater-specific features. Furthermore, a multi-scale feature alignment module is introduced to dynamically balance information across different resolution paths, enhancing consistency and contextual representation. Extensive experiments demonstrate that IA2U significantly improves both image enhancement and object detection performance. Specifically, on the UIEB dataset, IA2U boosts Shallow-UWNet by 5.2 dB in PSNR and reduces LPIPS by 52%; on the RUOD dataset, it increases AP by 1.8% when applied to the PAA detector. IA2U provides an effective and scalable solution for building robust underwater perception systems with minimal adaptation costs. Our code is available at https://github.com/zhoujingchun03/IA2U.

Underwater image quality assessment (UIQA) is a challenging task due to the complexities of underwater environments. Traditional UIQA methods primarily rely on fitting mean opinion scores (MOS), which are limited by human visual biases. To address the above limitation, we propose a no-reference underwater image quality assessment paradigm using reinforcement sequences. Our paradigm leverages reinforcement learning to iteratively merge the input image with the corresponding ground truth, generating an optimized sequence of images. A classifier generates probability arrays for the optimized sequence, which are converted into objective scores by a regression model. Unlike existing methods that focus solely on the final quality score, our paradigm emphasizes dynamic quality changes throughout the image-enhancement process. By employing objective mixing ratio labels, our reinforcement sequence dataset reduces subjective bias. The multiscale classifier captures local and global information differences between the input and ground truth images, effectively preserving the contrast and detail in diverse lighting conditions. Our paradigm combines multi-source data classification with support vector regression, optimizing the mapping of feature vectors to quality scores through fine-tuning libsvm kernel parameters. Experimental results on multiple benchmark datasets demonstrate that our paradigm outperforms the state-of-the-art UIQA methods, providing an effective solution for Underwater Image quality Assessment via Reinforcement Sequences (RSUIA).

Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. Our code is available at https://github.com/zhoujingchun03/SMDR-IS

Enhancing underwater images captured under mixed artificial and natural lighting conditions presents two critical challenges. Existing methods lack a unified luminance feature extraction paradigm for mixed lighting scenes, leading to imbalance in luminance features, and consequent local overexposure or underexposure. Additionally, some color correction methods, through the fusion of features across multiple color spaces neglect the information loss due to the absence of feature alignment in cross-space fusion. To address these challenges, we propose a specialized method, namely IACC, which unifies the luminance features of underwater images under mixed lighting and guides consistent enhancement across similar luminance regions. Furthermore, complementary colors are introduced to globally guide the correction of color discrepancies, preserving the structural consistency and mitigating potential structural information loss during the original image feature extraction. Extensive experiments on various underwater datasets demonstrate the superiority of our method, which outperforms state-of-the-art methods in both machine and human visual perception. Our code is available athttps://github.com/zhoujingchun03/IACC.

Water-related optics images are often degraded by absorption and scattering effects. Current underwater image enhancement (UIE) methods improve image quality but neglect the constraints of underwater imaging environments. To address this issue, we propose a double-teacher knowledge distilling network (DTKD-Net), which uses a dynamic teaching strategy within a dual-teacher framework to enhance knowledge distillation (KD), improving the student network’s ability to capture complex underwater features. Specifically, DTKD-Net focuses on clear-to-clear and blurry-to-clear image learning to enhance underwater images. It aims to preserve details in clear images and restore blurred ones. The dual-teacher network uses an intermediate layer with the middle layer of the student network to compute feature differences for feature guidance. The network uses a dynamic strategy where a Teacher-Sub stops guidance when its output matches the student’s, which helps with contrastive learning and improves the network’s ability to handle complex underwater scenes. Extensive experiments and visual comparisons show that DTKD-Net reduces the model size, demonstrating superior efficiency and effectiveness in enhancing underwater images.

Light traveling through water results in strong scattering across color channels, restricting visibility in underwater images. Many cutting-edge underwater image enhancement methods encounter limitations in color recovery accuracy and resilience against irrelevant feature interference. To tackle these degradation challenges, we propose an efficient and fully guided information flow network called UGIF-Net, for enhancing underwater images. Specifically, we propose a multi-color space-guided color estimation module that accurately approximates color information by incorporating features from two color spaces within a unified network. Subsequently, we employ a dense attention block to guide the network in thoroughly extracting color information from both color spaces while adaptively perceiving crucial color information. Moreover, we devise a color-guided map to steer the network’s focus toward color information and augment its response to color quality degradation. We incorporate the guided map into a guide color restoration module to achieve visually appealing enhancement results. Comprehensive experiments indicate that our approach surpasses state-of-the-art methods, showcasing favorable image restoration effects and their potential to aid other high-level vision tasks.

The red spectrum is saliently attenuated due to the absorption and scattering properties of water. The acquired underwater images show severe color cast in underwater scenes. In this letter, we propose a novel color correction method for underwater images, which removes color cast on single pixels based on scene depth. The experimental results demonstrate that our approach can significantly improve the color effect and provide a correct input for the subsequent underwater image defogging methods.

In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.