To address the issues of distortion in thin fog areas and incomplete dehazing in dense fog areas present in current dehazing algorithms, we propose an image dehazing algorithm that combines fog concentration segmentation with atmospheric light mapping. First, we analyze the fog concentration distribution in different regions of the image and construct a fog concentration estimation model using saturation and chromiance, a fuzzy clustering algorithm is then applied for region segmentation, effectively identifying thin and dense fog areas. Next, based on the relationship between fog concentration and atmospheric light, we design specific atmospheric light estimation models for different regions to ensure accurate processing of various fog concentration areas. Finally, by utilizing the brightness component of the fog concentration, we improve the estimation of local atmospheric light and obtain a fog-free image based on the atmospheric scattering model. The experimental results indicate that the algorithm effectively addresses the poor image restoration performance in non-uniform foggy conditions. Compared to current mainstream dehazing algorithms, it achieves improvements of 39% in visible edge increase rate, 28% in normalized average gradient, 10% in image entropy, 20% in visibility balance metric, 37% in visual contrast, 47% in image contrast, and 35% in computation time.