文献类型：期刊文献

英文题名：A Consistency-Guided Collaborative Filtering Framework for Suppressing Structured Coherent Artifacts

作者：Wang, Rui[1,2];Zhang, Peizhen[1];Li, Canping[1];Zhang, Hairong[1];Gong, Xiangbo[2];Hu, Bin[2]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Jilin Univ, Key Lab Geophys Explorat Equipment, Minist Educ, Changchun 130026, Peoples R China

年份：2026

卷号：18

期号：16

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001860493800001)、、EI(收录号：20263521401336)、Scopus(收录号：2-s2.0-105048736796)、WOS

基金：This research was supported by the Zhanjiang Municipal Special Project for Marine Youth Talent Innovation (No. 2025R02101), the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project (No. 2024ZD1004101), the Special Fund of Key Laboratory of Geophysical Exploration Equipment, Ministry of Education (Jilin University) (GEIOF20250103), the Guangdong Association for Science and Technology Youth Science and Technology Talent Cultivation Program (2026-2027) (No. SKXRC2026524), the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012041), and the Supported by Program for Scientific Research start-up funds of Guangdong Ocean University (No. 06032112311).

语种：英文

外文关键词：collaborative filtering; consistency-guided denoising; structured coherent artifacts

外文摘要：Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability.