We present a novel neighborhood filter (NF)-based clutter removal algorithm in ground-penetrating radar (GPR) images.
Since NF uses only range kernel of the well-known bilateral filter, it is less complex and makes clutter removal method
appropriate for real-time implementations. We extend NF to multiscale–multidirectional case: MDNF and then decompose
the GPR image into approximation and detail subbands to capture the intrinsic geometrical structures that contain both target
and clutter information. After directional decomposition, the clutter is eliminated by keeping the diagonal information for
target component. Finally, the inverse transform is applied to the remaining subbands for reconstruction of clutter-free GPR
image. Results of both simulated and real datasets validate the superiority of MDNF over the state-of-the-art methods, and it
improves in the false alarm rate further by 5.5% at maximum detection performance.