A PixelCNN Based Method for Rough Surface Clutter Reduction in GPR B-scan Images

Reducing the rough surface clutter in a Ground Penetrating Radar (GPR) B-scan image is essential for detecting shallow buried targets and for improving the performance of subsequent image processing algorithms. Challenges involved in this problem lie in the difficulties with modeling the variations of the rough surface profile. In this paper, we propose a novel PixelCNN based method for reducing rough surface clutter in GPR B-scan images. In the proposed method, the rough surface region in a B-scan image is split into small patches which are used to train a PixelCNN model. Given an input patch, the trained PixelCNN model can output the probability that the input patch comes from the rough surface region. For reconstructing a clutter reduced B-scan, the entire B-scan image is split into patches and each patch is input into the trained PixelCNN model to get its corresponding probability. Negative log-probabilities are then utilized as scores to suppress the rough surface region and to enhance the target profile in the B-scan image. To demonstrate the effectiveness of the proposed method, we test it on four simulation B-scan images and two real-world B-scan images. The proposed method is compared with traditional subspace projection methods. The results indicate that the proposed method outperforms traditional subspace projection methods.