Abstract:

Aiming at the problem that the traditional sparse feature enhancement method can only enhance the salient points in the target scene and cannot do anything about the complex target structure features, considering the complexity of the target detail features, a directional total structure variation (DTSV) regularizer is proposed for a priori structural feature. The fitting of arbitrary gradient change of complex structural features of imaging targets is realized, and then the high-precision regularization optimization processing of structural features is realized. Firstly, DTSV-alternating direction method of multipliers (DTSV-ADMM) is implemented under the collaborative optimization framework of ADMM. The dual ascent idea provided by this framework can effectively improve the convergence performance of iterative optimization algorithm. Secondly, based on the multivariable "decomposition-coordination" mechanism provided by ADMM framework, the cooperative optimization and enhancement of multiple regular terms can be realized by establishing split variable groups. Thirdly, the ${\ell _1}$ norm is further introduced to characterize the sparse features of imaging targets, and the robust calculation of directional structural features and sparse features is realized under the framework of collaborative optimization, so as to effectively reduce the problem of "error propagation" in multi feature optimization. Finally, the features are analytically calculated by the proximity operator to obtain the closed analytical solution of the corresponding features, which further improves the computational robustness and computational efficiency of the algorithm. Experiments show that the proposed algorithm is superior to the conventional method.

Key words: synthetic aperture radar (SAR), directional total structure variation (DTSV), alternating direction method of multipliers (ADMM), multi-feature enhancement, proximity operator