Single image dehazing remains challenging because haze simultaneously distorts global illumination, scene structure, and fine textures, making rigid low–high frequency decoupling prone to error propagation and detail inconsistency. To address this issue, we propose CoFiWaveMamba, a coarse-to-fine wavelet-guided Mamba network for single image dehazing. The proposed method first employs wavelet decomposition to separate low- and high-frequency components. For low-frequency restoration, a 2D selective-scan Mamba-based module is introduced to capture long-range dependencies, combined with lightweight high-frequency-guided spatial modulation and Shuffle-guided Sequence Attention, we design a progressive coarse-to-fine refinement strategy that combines Fourier-domain global spectral consistency with wavelet-domain directional detail representation, enabling more targeted recovery of edges and textures. Experiments on synthetic and real dehazing benchmarks, including Haze4K, RESIDE-6K, HSTS-SYNTHETIC, I-Haze, NH-Haze, Dense-Haze, and O-HAZE, as well as ablation studies, verify the effectiveness of the proposed design. Overall, CoFiWaveMamba provides a more coordinated solution for global haze removal and local detail reconstruction, helping suppress residual haze, ringing artifacts, oversharpening, and texture inconsistency while restoring clearer and more natural images.

The study proposes a real-time fatigue detection method based on deep perception technology, specifically designed for Unmanned Aerial Vehicle (UAV)operators to ensure safe operation. The approach leverages Kinect 2.0 to capture the facial and posture information of the operator. Feature extraction is performed using a Convolutional Neural Network, in combination with the percentage of eyelid closure rate over time (PERCLOS) algorithm, to accurately identify fatigue states. Experimental results demonstrate that this method achieves an accuracy rate of 93.3% in fatigue detection, outperforming traditional methods. Future work will focus on further optimizing the model structure and integrating multimodal data to enhance the system’s precision and adaptability, providing technological support for UAV safety management.