• 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.

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 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).

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.