Joint Super-Resolution and Weighted Low-Rank Sparse Decomposition for Ground Penetrating Radar Clutter Suppression

Ground Penetrating Radar (GPR) is an important non-destructive sensing technology for subsurface detection. However, strong clutter from surface reflection and heterogeneous media often masks weak target responses in B-scan images. Existing low-rank and sparse decomposition (LRSD) methods, such as Robust Principal Component Analysis (RPCA), can separate clutter and targets but suffer from uniform singular value shrinkage and high computational cost; more critically, they operate only at the original resolution, refining only the spatial sampling grid of the migrated image under the inherent physical resolution limit. In this paper, we propose a Joint Super-Resolution and Weighted Low-Rank Sparse Decomposition (JSR-LRSD) framework that synergistically suppresses clutter and improves the spatial detail and target localization of the migrated image without extending its physical resolution. Unlike cascade approaches, which perform clutter removal first and super-resolution afterward and may amplify residual clutter, our method integrates a differentiable super-resolution network directly into the alternating direction method of multipliers (ADMM) iterations. This allows for the sparse target matrix to be regularized by high-resolution image priors, while the high-resolution estimate adaptively adjusts the weighted nuclear norm via total variation feedback. An adaptive weighted nuclear norm is designed using singular values of the observed data and the total variation feedback from the high-resolution target estimate. A randomized singular value decomposition (SVD) is introduced to accelerate computation. Extensive experiments on simulated and laboratory GPR data demonstrate that the proposed JSR-LRSD achieves a higher signal-to-clutter ratio (SCR) compared to conventional and LRSD-based methods, with clear improvement in spatial detail recovery, while maintaining computational efficiency.