GPR image denoising with NSST-UNET and an improved BM3D

To suppress random noise while preserving effective information in the edge areas of ground penetrating radar (GPR) images, this paper proposes a novel denoising method by making use of a deep neural network called NSST-UNET and an improved BM3D. At first, NSST-UNET is designed with a non-subsampled shearlet transform (NSST) coding layer and a skip connection based on a multi-scale convolution module and applied to identify the edge and smooth areas of noisy GPR images. Then, the denoising is accomplished with the improved BM3D in two steps. In the first step, a larger search range for similar blocks and a soft threshold are used to denoise the edge and smooth areas, respectively. In the second step, the Wiener filter optimized by mean square error and the Wiener filter optimized by structural similarity are utilized to denoise the smooth and unsmooth areas, respectively. Finally, the excellent denoising performance of the proposed method is verified by qualitative and quantitative analysis with simulation and field exploration data.