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Article

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, China
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Remote Sens. 2020, 12(22), 3714; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223714
Received: 2 October 2020 / Revised: 5 November 2020 / Accepted: 7 November 2020 / Published: 12 November 2020
(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)
Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method. View Full-Text
Keywords: stripe noise; thermal infrared (TIR); Fourier transform; anomaly detection; spectral fusion; conventional TIR images; remote sensing spectral images stripe noise; thermal infrared (TIR); Fourier transform; anomaly detection; spectral fusion; conventional TIR images; remote sensing spectral images
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MDPI and ACS Style

Zeng, Q.; Qin, H.; Yan, X.; Yang, T. Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery. Remote Sens. 2020, 12, 3714. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223714

AMA Style

Zeng Q, Qin H, Yan X, Yang T. Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery. Remote Sensing. 2020; 12(22):3714. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223714

Chicago/Turabian Style

Zeng, Qingjie, Hanlin Qin, Xiang Yan, and Tingwu Yang. 2020. "Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery" Remote Sensing 12, no. 22: 3714. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223714

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