Underwater light absorption and scattering lead to severe color distortion, reduced visibility, contrast loss, and a significant degradation in image quality, thereby impeding both human visual analysis and machine vision tasks. Although considerable progress has been achieved in improving image quality, existing deep learning-based methods for underwater image enhancement (UIE) remain constrained by high computational complexity and insufficient modeling of global dependencies, which restricts their practical deployment in resource-limited underwater environments. To tackle these issues, we propose a novel hybrid framework integrating Retinex theory and state-space models (SSMs) for underwater image enhancement, named HRMamba. Different from existing Transformer-based approaches constrained by quadratic complexity, HRMamba attains computational efficiency through linear-complexity state-space operations while maintaining global dependency modeling capabilities. Moreover, to achieve comprehensive feature fusion, an Illumination Feature Fusion Module (IFFM) is proposed, which synergizes the global dependency modeling of SSMs with the local adaption capability of convolutional neural networks (CNNs). For context-sensitive noise suppression with illumination awareness, we propose an Illumination-Guided Denoising Module (IGDM) that employs directional-scanning Vision State Space Module (VSSM) blocks. Experiments demonstrate that HRMamba achieves state-of-the-art enhancement quality via an efficient architecture, significantly improving color fidelity and visibility restoration while substantially reducing computational demands. The code is available at https://github.com/YeFan-web/HRMamba/.

The absorption and scattering of light caused by water and suspended particles in the ocean may lead to underwater image degradation. Existing deep learning-based underwater image enhancement models suffer from an excessive number of parameters, which makes them unsuitable for deployment on resource-constrained underwater Internet of Things (IoT) devices. Furthermore, the evaluation methods for enhanced results may not consider the feature richness. In this work, a novel lightweight underwater image enhancement framework and three baseline models based on the framework are proposed. The proposed framework is inspired by the atmospheric imaging model and histogram differences, and it believes that the reference image can be expressed as a linear combination of the degraded image and two nonlinear transformations, termed as the compensation map and the suppression map. This framework compels the neural network model to use the learned parameters to represent the nonlinear relationships among the degraded image, compensation map, and suppression map, rather than to generate high-level semantic information within an encoder-decoder framework. As a result, the number of parameters is significantly reduced. This paper also proposes a novel subjective evaluation method for underwater images, termed as Feature Entropy Evaluation (FEE). The proposed FEE method outperforms existing evaluation methods for underwater scenes with color checker plates by delivering rankings that more accurately capture the absolute value differences in the color checker plates. Extensive experiments on public datasets demonstrate that, as compared to other existing methods, the proposed models achieve comparable results with only 0.01% parameters of existing methods. The relevant code will be made available after the paper is accepted.

This paper presents an underwater acoustic target recognition method to reduce recognition errors in continuous recordings caused by variations in ship operating conditions. The proposed method comprises two-stages: the spectral feature classification and the supervised contrastive learning, and it is called as SFC-Sup as a result in this paper. In the first stage, a new spectral feature classification strategy is designed to choose appropriate feature sets for contrastive learning, based on which an instance discrimination pretext task is created by utilizing different spectral features to capture invariant features across segments under different operating conditions. In the second stage, a dynamic weighted loss function is introduced to guide the joint optimization process in the framework of contrastive learning. Different to existing methods which focus on improving the recognition accuracy by designing features for individual segments, the proposed two-stage method SFC-Sup considers consistent features across diverse segments, which is expected to improve recognition accuracy in a continuous recording. Experimental results demonstrate that in the presence of complex operating conditions, SFC-Sup exhibits superior stability and enhances recognition accuracy by 2.06% compared to state-of-the-art methods.