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Hyper-wavelet transforms such as the non-subsampled shearlet transform (NSST) are popular algorithms to remove random noise and clutter in ground penetrating radar (GPR) images. However, due to the effect of time-varying gain, random noise and clutter in GPR images are non-stationary. Moreover, random noise which is poorly correlated among pixels and clutter which shows a certain correlation among adjacent pixels are usually mixed together. Therefore, it is difficult to determine the threshold function of hyper-wavelet transforms, resulting in a decrease of noise and clutter removal performance. To address this issue, a novel two-step algorithm is proposed to remove non-stationary random noise and clutter in the GPR image separately. In the first step, Self2Self, a self-supervised denoising algorithm, is employed to remove the non-stationary random noise. In the second step, a time-varying threshold function based on NSST and an edge area protection method based on the Canny algorithm are proposed to effectively remove non-stationary clutter in the GPR image. Experimental results show that the proposed method has an excellent performance in removing non-stationary random noise and clutter while effectively protecting the edge information of the GPR image.

Separate removal of random noise and clutter in GPR images based on Self2Self and NSST