A complex structure feature enhancement (CEA) algorithm was proposed, and in which the complex structure is represented by high-order total direction variation (HOTDV) regular operator, and the sparse feature of SAR imaging targets is represented by 𝓁1 regular operator. The algorithm uses Alternating Direction Method of Multipliers (ADMM) to establish a multi-regular constraint optimization framework, and designs complex structure splitting variables and sparse splitting variables. The analytic solutions of split variables are obtained to enhance the complex structure features and sparse features of SAR imaging targets. The thinking of “dual-decomposition” in the multi-regular constraint optimization framework guarantees the multi-feature processing capability, while the use of Augmented Lagrange term guarantees the convergence and robustness of the algorithm. Finally, simulation and measured SAR data complex structure feature enhancement experiments are designed to verify the effectiveness of the proposed algorithm, and several traditional structure feature enhancement algorithms are compared to verify the superiority of the proposed complex structural feature enhancement algorithm.