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Principal Component Analysis-Based Terahertz Self-Supervised Denoising and Deblurring Deep Neural Networks
- Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties andTerahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties and measurement modes demonstrates effective denoising and deblurring. Quantitative analysis further validates the network’s feasibility, showing improvements in image quality while preserving the physical characteristics of the original signals.…


| Autor*innen: | Pengfei ZhuORCiD, Xavier Maldague |
|---|---|
| Dokumenttyp: | Preprint |
| Veröffentlichungsform: | Graue Literatur |
| Sprache: | Englisch |
| Titel des übergeordneten Werkes (Englisch): | arXiv.org |
| Jahr der Erstveröffentlichung: | 2026 |
| Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
| 8 Zerstörungsfreie Prüfung / 8.3 Thermografische Verfahren | |
| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Cornell University |
| Verlagsort: | Ithaca, NY |
| Aufsatznummer: | arXiv:2601.12149 |
| Erste Seite: | 1 |
| Letzte Seite: | 9 |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau |
| Freie Schlagwörter: | Deblurring; Denoising; Non-destructive testing (NDT); Self-supervised learning; Terahertz |
| Themenfelder/Aktivitätsfelder der BAM: | Infrastruktur |
| Infrastruktur / Leichtbau | |
| DOI: | 10.48550/arXiv.2601.12149 |
| URN: | urn:nbn:de:kobv:b43-654043 |
| ISSN: | 2331-8422 |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Allgemeines Deutsches Urheberrecht |
| Datum der Freischaltung: | 26.01.2026 |
| Referierte Publikation: | Nein |
| Schriftenreihen ohne Nummerierung: | Preprints der BAM |


