TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

Abstract

With the rapid development of image dehazing algorithms, the demand for effective dehazing solutions across various industries has markedly increased. However, the application effectiveness of most existing image dehazing algorithms within coal mine environments remains suboptimal. Consequently, this paper proposes an image dehazing algorithm based on a threshold multi-channel inspection method. The algorithm detects fog density using an enhanced color attenuation prior method, followed by image enhancement in fog-free areas and dehazing in foggy areas through threshold multi-channel inspection. During fog density detection, the algorithm incorporates texture information and illumination invariance features from the HSV space, enhancing adaptability and robustness to different lighting conditions. In the dehazing process, segregating foggy and fog-free images facilitates more accurate and reliable dehazing outcomes. Moreover, by constructing a multi-scale pyramid and employing a guided filtering approach, the algorithm achieves more precise estimation of the image transmittance, mitigates the blocky artifacts common in traditional methods. For video dehazing, a parameter reuse mechanism based on inter-frame similarity is designed, improving the real-time performance of video dehazing. The algorithm was tested on a coal mine dataset and on partial public datasets such as NH-Haze2 and Dense-Haze, achieving experimental results that surpass other algorithms.