Image-to-image color inconsistencies present a significant challenge in image-based 3D reconstruction, stemming from factors such as weather conditions, temporal variations, and exposure settings. Existing global color consistency correction algorithms exhibit limitations in simultaneously achieving effective color correction and efficiency, particularly in large-scale scenes. To address this issue, we propose an iterative strategy-based algorithm for multiview image color consistency correction. The algorithm initiates feature extraction and matching processes to establish point correspondences across images. For each set of correspondences, a priori color information is computed for each image. Subsequently, quadratic splines are computed by quadratic convex optimization for individual images. The raw pixel values are updated using these splines, and this iterative process is repeated to compute a series of quadratic splines until convergence. Simultaneously, gradient constraints are introduced to ensure that the image retains contrast. By applying these computed quadratic splines to transforming the images, we can achieve color consistency correction. Experimental results demonstrate that the proposed algorithm not only achieves higher quality than existing algorithms but also significantly improves operational efficiency by reducing the complexity of the algorithm. The inherent flexibility from the use of simultaneous splines facilitates the acquisition of a series of splines through iterative computations, further enhancing its color correction capability. This methodology can find broad applications in image stitching, image-based 3D reconstruction, and other related fields.