To address the issue of poor performance of traditional image quality enhancement algorithms across different scenes, a novel image quality enhancement method based on dynamic adaptive optimization model is proposed to meet the diverse requirements of various scenes and improve the effectiveness of image quality enhancement. Firstly, a dynamic adaptive optimization model is constructed based on the atmospheric scattering characteristics of the enhanced image. And the objective function of the model is designed using image quality assessment metrics, PSNR and SSIM, to provide evaluation standards for image quality enhancement in different scenes. Based on this, a cooperative-competitive learning operator is designed and cooperative-competitive human learning optimization algorithm is proposed to calculate the optimal transmission threshold t0, filtering window size n, and weighting parameter ω. Then the optimal dynamic adaptive optimization model is constructed to achieve image quality enhancement in different scenes. Finally, image quality enhancement experiments are conducted using images from the SOTS benchmark test set and six real scene images. The proposed method is compared with three other methods, i.e. CLAHEMF, IDCPLT and DCP-PSO. Experimental results demonstrate that the proposed method outperforms the three comparison methods in terms of both subjective visual effects and objective evaluation metrics, thereby fully validating the effectiveness and feasibility of the proposed approach.