Abstract: To overcome the limitations of the dark channel prior dehazing algorithm, such as color distortion and Halo artifacts, this study proposes an image dehazing method based on contrast enhancement and a non-uniform atmospheric scattering model. First, the hazy image undergoes adaptive histogram equalization with color preservation to enhance contrast and color saturation. Second, considering the non-uniform distribution of atmospheric light, a non-uniform atmospheric light estimation strategy is introduced. A coarse estimation is initially obtained and subsequently refined using a guided filter. The transmission map is further optimized using a weighted least squares filter to suppress block artifacts while preserving image details. Finally, the dehazed image is reconstructed using the non-uniform atmospheric scattering model. Both qualitative and quantitative experimental results show that the algorithm effectively removes haze, producing images with natural color representation and clear, detailed textures, outperforming existing methods.