In the existing color image deblurring process,there are such phenomena as color imbalance,step effect and artifacts.To solve the problems,an image deblurring optimization method based on fractional total variation and low-rank regularization is proposed.Firstly,the color image in traditional RGB space is converted to YCbCr color space,and its luminance channel characteristics are used to solve the problem of color imbalance.Secondly,fractional total variation characteristics are used to eliminate step effect in image recovery tasks.Moreover,the weighted kernel norm low-rank regularization is introduced to further suppress artifacts and noise.Finally,the Alternating Direction Method of Multipliers (ADMM) is used to design an efficient solving method,and the optimal estimation of the clear image is obtained through iterative optimization.The experimental results of color image testing show that the proposed method exhibits fine performance in image deblurring tasks in terms of both visual recovery effects and objective evaluation indexes.