Ground penetrating radar data reconstruction via matrix completion

Missing information in ground-penetrating radar (GPR) data is a commonly encountered phenomenon during field measurements and it highly affects the subsequent processing steps of the GPR data, such as migration, clutter removal, target identification, etc. Since the GPR field tests are time consuming and hard to repeat, it is better to use the measured data even if they contain partial missing information. Due to the common problems in GPR field measurements, the missing information can occur in both pixel and column-wise and there are many methods proposed to solve them. In this study, we selected the best matrix completion methods for GPR data which provide satisfactory results. Some of the methods are already applied to GPR problem however there are no extensive comparisons available and recently proposed ones are for the first time used, which are the novelty of this study. Among these methods, nuclear norm minimization (NNM) and non-negative matrix completion (NMC) outperform others for the pixel-wise and the column-wise cases. Both simulated and real dataset results show that for moderate missing information cases NNM can be selected however for extreme cases NMC gives better results.