Adaptive multi-channel dehazing for enhanced visibility in underground coal mine images.

Image dehazing has gained significant attention due to its importance in enhancing image clarity in various applications. However, existing algorithms often struggle with suboptimal performance in underground coal mine environments, characterized by dim lighting and atmospheric interference. This paper presents an adaptive multi-channel dehazing algorithm tailored for enhancing images from underground coal mines. By utilizing an improved color attenuation prior method, the algorithm effectively detects fog density, incorporating texture information and illumination invariance features from the HSV space for enhanced adaptability and robustness. The algorithm segregates foggy and fog-free image regions, applying image enhancement in clear areas and threshold multi-channel inspection dehazing in foggy regions. A multi-scale pyramid and guided filtering approach are employed to refine the estimation of image transmittance, mitigating blocky artifacts. For video dehazing, a parameter reuse mechanism leveraging inter-frame similarity significantly improves real-time performance. Experimental results on coal mine datasets and public benchmarks demonstrate that the proposed algorithm outperforms existing methods in defogging effectiveness, computational efficiency, and stability, rendering it suitable for real-time applications such as safety monitoring in underground coal mines.