A dictionary learning–enhanced deep convolutional network for GPR image noise suppression

Abstract Ground‐penetrating radar (GPR) is a widely used technique for near‐surface exploration, providing subsurface imaging of underground targets. However, in practical surveys, random noise severely degrades image quality and compromises interpretational reliability. Conventional GPR denoising approaches often rely on manual parameter tuning and struggle to achieve an effective balance between noise suppression and feature preservation. To address this challenge, this study proposes a hybrid denoising model (HybridDenoiser) that integrates dictionary learning with deep convolutional networks. The model employs an encoder to extract multi‐level features, incorporates a learnable dictionary to achieve sparse representation of these features and uses a decoder for high‐fidelity image reconstruction. Experimental results on both synthetic and field data demonstrate that the proposed method effectively suppresses noise, significantly enhances signal‐to‐noise ratio and structural similarity and better preserves the reflection characteristics of subsurface targets. These findings confirm the model's strong practicality and generalization capability, offering a promising solution for improving GPR image quality in complex environments.