An improved image dehazing method is proposed to address the issues of blurred details and color distortion in the restored image obtained by traditional dehazing algorithms. Firstly, the main structural image and the minimum channel image of the hazy image are acquired. The brightness mapping of the main structural image is calculated, and the maximum and sub-maximum points are identified to form four candidate regions. The median value of the region with the minimum entropy is selected as the global atmospheric light value. Subsequently, a fog line reliability evaluation parameter is introduced to determine whether transmission rate points belong to the noise area. Clustering of transmission rate points in the noise area is conducted, and the transmission rate is optimized using neighborhood fog lines. Clusters with too few pixels are merged, and the selection range of the maximum irradiance is appropriately expanded to compensate for errors caused by limited areas. Finally, edge information in the minimum channel is extracted using a side window box filter. An adaptive weight factor based on the characteristics of fog line clustering results is designed to remove texture information, further refine the transmission rate, and ultimately obtain the restored image based on the atmospheric imaging model. Experimental results demonstrate that the proposed algorithm shows significant performance improvements compared with various dehazing algorithms, with enhancements in information entropy, average gradient, blur coefficient, and fog concentration evaluation index (FADE), resulting in more complete details and better color matching with human visual perception in the restored image.