The work aims to improve the brightness distribution in the image enhancement algorithm, suppress the pseudo halo and noise, and improve the detail and brightness of enhancement image. An image enhancement scheme based on retinex filter coupling post-processing optimization was proposed. Firstly, a guided filter was used to estimate the illumination of the input image, and the smooth Y channel in the YCbCr color space was used as the guided image to effectively capture the brightness of the real scene and calculate the image illumination accurately. Secondly, according to the estimated illumination, the reflectivity of the image scene was extracted. In order to provide dynamic range compression and tone reproduction at the same time, a multi-scale Retinex and color repair operator was designed, which used three different scales of Gaussian filter weighting combination to enhance color image. Finally, to achieve the optimal performance of multi-scale Retinex and color repair operators, a learning strategy was explored through the automatic post-processing method consisting of nonlinear stretching and parameter optimization. The optimal parameters of each input image were determined adaptively by quantum particle swarm optimization (QDPSO), thus effectively considering the relationship between the illumination and the reflectivity of the scene, and avoiding the color distortion. The experimental data showed that, compared with the current commonly used enhancement algorithms, the proposed algorithm obtained higher clarity and details of the enhanced image, which was more aligned with the visual perception characteristics, and its efficiency was higher. The time consumed was about 0.7 s. With good enhancement effect, the proposed algorithm has certain reference value in the field of image information processing.