# Multi-Frequency GPR Image Fusion Based on Convolutional Sparse Representation to Enhance Road Detection

## TL;DR

This paper introduces a new method to improve road detection using GPR by combining data from multiple frequencies with a technique called convolutional sparse representation.

## Contribution

The novel contribution is a multi-frequency GPR image fusion technique based on convolutional sparse representation that enhances image clarity.

## Key findings

- The CSR-based fusion method outperforms traditional approaches like PCA and 2D wavelets in image quality metrics.
- The method successfully combines the depth of low frequencies with the resolution of high frequencies.
- Tests on simulated and real data show improved GPR image clarity and interpretability.

## Abstract

Single-frequency ground penetrating radar (GPR) systems are fundamentally constrained by a trade-off between penetration depth and resolution, alongside issues like narrow bandwidth and ringing interference. To break this limitation, we have developed a multi-frequency data fusion technique grounded in convolutional sparse representation (CSR). The proposed methodology involves spatially registering multi-frequency GPR signals and fusing them via a CSR framework, where the convolutional dictionaries are derived from simulated high-definition GPR data. Extensive evaluation using information entropy, average gradient, mutual information, and visual information fidelity demonstrates the superiority of our method over traditional fusion approaches (e.g., weighted average, PCA, 2D wavelets). Tests on simulated and real data confirm that our CSR-based fusion successfully synergizes the deep penetration of low frequencies with the fine resolution of high frequencies, leading to substantial gains in GPR image clarity and interpretability.