This paper proposes a new single image haze removal algorithm based on the dark channel prior and adaptive transmittance. The algorithm aims to improve the clarity, brightness, and color accuracy of images degraded by haze. The method involves partitioning the image into local regions to calculate the dark channel and minimum map. The atmospheric light is estimated using the dark channel map, and the transmittance is calculated using an atmospheric dissipation function. The median guide filtering algorithm is then used to distinguish between close-range and remote areas, and the transmittance of the close-range area is corrected to obtain accurate transmittance estimation. The restored image is then obtained based on the atmospheric scattering model. The algorithm addresses the limitations of traditional methods, which often fail to accurately estimate transmittance in bright areas, leading to reduced brightness and color distortion. To solve this, an adaptive transmittance method is introduced, which uses the atmospheric dissipation function to estimate and modify the transmittance adaptively. This method improves the accuracy of transmittance estimation, especially in bright regions, leading to better image restoration. Experimental results show that the proposed algorithm significantly improves the quality of the restored image. Compared to traditional algorithms, the fuzzy coefficient is reduced by 23.09%, and the structural similarity and tone reduction are increased by 14.63% and 12.29%, respectively. The algorithm effectively enhances the clarity, brightness, and color accuracy of the restored image, making it more consistent with the original scene. The method is based on the dark channel prior theory and adaptive transmittance, which significantly improves the quality of the restored image.This paper proposes a new single image haze removal algorithm based on the dark channel prior and adaptive transmittance. The algorithm aims to improve the clarity, brightness, and color accuracy of images degraded by haze. The method involves partitioning the image into local regions to calculate the dark channel and minimum map. The atmospheric light is estimated using the dark channel map, and the transmittance is calculated using an atmospheric dissipation function. The median guide filtering algorithm is then used to distinguish between close-range and remote areas, and the transmittance of the close-range area is corrected to obtain accurate transmittance estimation. The restored image is then obtained based on the atmospheric scattering model. The algorithm addresses the limitations of traditional methods, which often fail to accurately estimate transmittance in bright areas, leading to reduced brightness and color distortion. To solve this, an adaptive transmittance method is introduced, which uses the atmospheric dissipation function to estimate and modify the transmittance adaptively. This method improves the accuracy of transmittance estimation, especially in bright regions, leading to better image restoration. Experimental results show that the proposed algorithm significantly improves the quality of the restored image. Compared to traditional algorithms, the fuzzy coefficient is reduced by 23.09%, and the structural similarity and tone reduction are increased by 14.63% and 12.29%, respectively. The algorithm effectively enhances the clarity, brightness, and color accuracy of the restored image, making it more consistent with the original scene. The method is based on the dark channel prior theory and adaptive transmittance, which significantly improves the quality of the restored image.