In image inpainting, it is crucial that the identification and inpainting of local detail features and the preservation of global features. The models based on fractional-order partial differential equations are characterized by rich evolutionary behaviors, which allow image details to be effectively understood and a certain sharpening effect to be exhibited in image inpainting. However, issues such as inaccurate identification of large-scale features and over-sharpening are prone to be encountered. An optimal control model is proposed in this paper and the objective function is defined by the total variation energy of image global features and the constraint is formulated by a spatial fractional-order vector-valued Cahn–Hilliard equation, aiming to achieve a balanced effect between local detail restoration and preservation of global features. L2 gradient flow, H-1 gradient flow, and convex splitting are applied to design a numerical scheme for non-convex constraint conditions. And then the Split Bregman method is used to optimize the objective function with a dynamic grayscale adjustment strategy is introduced to maintain grayscale discrimination capability while enhancing computational efficiency. The numerical experiments demonstrate that the new model achieves an improvement on PSNR values ranging from 0.3718dB to 9.9352dB compared to other methods, exhibiting strong competitiveness in terms of SSIM values and greater effectiveness on images with fragmental damages. Moreover, compared to traditional fractional-order equation models, the computational time is reduced by a factor of 0.4950 to 0.5291.