This study proposed an adaptive image dehazing algorithm designed to address the limitations of the traditional dark channel dehazing algorithm, which struggles to adapt to different scenarios. The proposed algorithm first performs brightness and darkness segmentation on the image, followed by an improved method for determining atmospheric light values and transmittance for preliminary dehazing. Next, the processed image is fused with the image preprocessed using limited contrast adaptive histogram equalization and subsequently subjected to gamma correction to obtain the final dehazed image. Experimental validation was conducted using publicly available datasets and images collected from real-life scenarios. The results demonstrate that the proposed algorithm owns an effective dehazing performance, considerably enhancing image quality. Compared with the traditional dark channel dehazing algorithm, the images processed by our algorithm exhibit a 44.1% improvement in peak signal-to-noise ratio, a 32.2% increase in structural similarity, a 2.5% boost in information entropy, a 9.5% enhancement in visible edge normalization gradient mean, and a 33.7% increase in clarity measurement index.