Advanced Adaptive Median Filter for Reducing Salt-and-Pepper Noise in GPR Data

作     者：Wang, Wentian Du, Wei Li, Yabin Jia, Zhuo 

作者机构：Inst Disaster Prevent Hebei Key Lab Earthquake Dynam Sanhe 065201 Peoples R China Yunnan Univ Sch Earth Sci Chenggong Campus Kunming 650106 Yunnan Peoples R China Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130012 Jilin Peoples R China Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Hunan Peoples R China 

出 版 物：《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS》 (IEEE Geosci. Remote Sens. Lett.)

年 卷 期：2025年第22卷

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Fundamental Research Funds for the Central Universities [ZY20230217] Science and Technology Support Project of Langfang City [2023011012, 2022013082, 2023011103] National Natural Science Foundation of China 

主　　题：Noise Filtering Wavelet transforms Reflection Noise measurement Ground penetrating radar Filtering algorithms Synthetic data Surface roughness Rough surfaces Adaptive median filtering ground-penetrating radar (GPR) random noise suppression signal processing 

摘      要：Due to the influence of both the observation environment and the instruments themselves, ground-penetrating radar (GPR) data are often contaminated by random noise, which degrades data quality. Salt-and-pepper noise is a common type of such noise. Adaptive median filtering is an effective technique for removing this noise. However, it has the drawback of replacing original values that are not affected by noise with the median, which can lead to a degradation in image quality. In this letter, we propose an improved adaptive median filtering method. First, we assess whether the original value is contaminated by salt-and-pepper noise. If the value is affected, filtering is applied. The window size is adaptively increased, and the window is subdivided into smaller sections. Multiple median calculations are then performed on the segmented windows to ensure the validity of the median. When the noise density is high, the median of the nonnoise points in the largest window is selected as the output, thereby minimizing the negative impact of noise on the median calculation. Both synthetic and real-world data validations demonstrate that the improved method significantly outperforms traditional adaptive median filtering, conventional median filtering, and other filtering methods, particularly in high-noise scenarios, thus confirming the superiority of the proposed algorithm.