Enhancing Deep Learning Networks Performance By Using B-Spline Activation Functions for Clutter Removal in GPR

Traditional clutter removal methods struggle with complex clutter and multiple targets in Ground-Penetrating Radar images. This study proposes using B-spline activation functions in the deep-learning models to improve clutter removal in GPR. Experimental results demonstrate that B-spline-enhanced models outperform their ReLU-based counterparts, with improvements of up to 2.20% in PSNR, 0.035% in MS-SSIM, and 15.83% in SCR, showcasing their potential for real-world GPR applications.