A Constrained Diffusion Model for Deep GPR Image Enhancement

Due to the electromagnetic propagation loss and environmental interference, the deep ground penetrating radar (GPR) image is poor for further interpretation. Many image enhancement methods including resolution improvement and clutter removal have been widely studied to improve the GPR image quality. In this letter, a deep GPR image enhancement method is proposed to generate clear high-resolution images by the diffusion model (DM). In order to ensure the model is equipped with abilities of resolution enhancement and declutter simultaneously, the low-frequency image inserted with clutter performances as prior knowledge input network to fit the Gaussian distribution clutter added in the forward process of the DM. The method has already been tested by simulation and field experiments. Compared with the classical methods of declutter only and improving the resolution only, our method achieves the best results by synthesizing entropy (EN), peak signal-to-clutter ratio (PSCR), and structural similarity (SSIM), which proves the effectiveness of resolution enhancement and clutter removal in the deep GPR image.