A multi-modal image dehazing enhancement algorithm based on the dark channel prior is proposed to address the limitations of single-mode processing and restricted generality in existing image enhancement methods. This algorithm is applicable to various polarization image modalities， including polarization intensity， Stokes parameters， and linear polarization images， as well as conventional RGB and grayscale images. For polarization images， atmospheric light estimation is performed through K-means clustering， grid partitioning， and bilinear interpolation， while atmospheric transmission is derived using brightness and structure-weighted techniques. Dark channel computation incorporates multi-scale Gaussian filtering combined with gradient-based adaptive weight fusion. For RGB and grayscale images， atmospheric light is estimated by K-means clustering and the 95th percentile of sky pixels， and atmospheric transmission is calculated via Gaussian Laplacian edge detection and bilinear interpolation. Dark channel computation utilizes multi-scale erosion operations alongside local contrast-based weighting. Experimental evaluation was conducted using multi-modal images collected under outdoor light mist and indoor artificial thick fog conditions， with dehazing enhancement outcomes compared against conventional dark channel prior and multi-scale Retinex algorithms. The results reveal marked improvements in image clarity， edge definition， and detail restoration. Specifically， polarization images demonstrated minimum enhancements of 112.6%， 14.0%， and 5.0% in average gradient， image entropy， and peak signal-to-noise ratio， respectively， relative to the multi-scale Retinex algorithm. Non-polarization images exhibited minimum improvements of 103.6%， 20.6%， and 21.9% across the same metrics. This comprehensive validation confirms that the proposed algorithm not only significantly enhances image quality but also maintains robust generality across diverse image modalities.