Laser pulses experience significant temporal broadening in underwater environments due to strong turbulence and scattering effects. As water turbidity increases, the likelihood of multiple scattering events rises, further intensifying pulse broadening and thereby degrading the ranging accuracy of underwater single-photon LiDAR systems. Accurate characterization of the return pulse shape is crucial for precise distance extraction, typically achieved via cross-correlation with the system’s Instrument Response Function (IRF). Conventional models often fail to accurately characterize the distinctive asymmetric shape of underwater LiDAR returns, which feature a rapid rise and a slow decay. To address this limitation, this paper proposes a Modified Biexponential Distribution (MBD) model, specifically designed to capture both the sharp leading edge and the gradual trailing decay of the pulses. This model enables a more accurate representation of the broadened pulse, effectively mitigating the ranging error induced by scattering. Experimental validation demonstrates that, at an attenuation length of 6.9, the Depth Absolute Error (DAE) is reduced from 3.82 cm to 3.15 cm (a 17.54% improvement), while the probability of achieving a DAE below 3.82 cm increases from 49.70% to 74.83%. These results confirm the effectiveness and robustness of the proposed model in enhancing the ranging accuracy of underwater photon-counting LiDAR systems. Furthermore, this study provides a model-driven analytical basis for improving underwater photon detection and bathymetric performance in turbid conditions.

Recent years have witnessed the emergence of numerous U-shaped deep learning segmentation methods for infrared small-target detection (IRSTD). However, increasingly complex models still suffer from false and missed detections in challenging scenarios with cluttered backgrounds and weak targets while incurring escalating computational costs. To address these limitations, this paper proposes MCC-Net, a novel and efficient IRSTD framework that achieves superior detection performance with significantly reduced computational complexity. First, we integrate Magnitude-Aware Linear Attention (MALA) and Conditionally Parameterized Convolutions (CondConv) to replace conventional attention mechanisms in skip connections and standard convolutions, respectively, endowing the model with spatial contextual modeling and enhanced feature extraction capabilities at minimal computational overhead. Second, we design an innovative Conditional Cross-Channel Fusion (CondCCF) module that establishes a complementary spatial-channel dual-attention mechanism with MALA, enabling efficient multi-scale feature fusion. Extensive comparative and ablation experiments conducted on three public benchmarks—SIRST-v1, NUDT-SIRST, and IRSTD-1K—demonstrate that MCC-Net achieves state-of-the-art mIoU scores of 77.98%, 95.43%, and 70.46%, respectively, surpassing state-of-the-art methods by 1.07%, 1.95%, and 0.95%. MCC-Net also outperforms existing approaches across multiple evaluation metrics while maintaining substantially lower computational complexity.