التفاصيل البيبلوغرافية
| العنوان: |
NL3DLogTNN: An Effective Hyperspectral Image Denoising Method Combined Non-Local Self-Similarity and Low-Fibered- Rank Regularization |
| المؤلفون: |
Haoran Liu, Tianyu Su, Xinzhe Du, Yuxin Zhai, Jianli Zhao |
| المصدر: |
IEEE Access, Vol 11, Pp 91082-91099 (2023) |
| بيانات النشر: |
IEEE, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Hyperspectral image denoising, non-local self-similarity, 3DLogTNN decomposition, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Hyperspectral image denoising is an important research topic in the field of remote sensing image processing. Recently, methods based on non-local low-rank tensor approximation have gained widespread attention towing to their ability to fully exploit non-local self-similarity. However, existing non-local low-rank tensor approximation methods fall short in capturing the correlations between various modes in hyperspectral images, thus failing to achieve the optimal approximation. To solve this issue, a novel three-directional log-based tensor nuclear norm (3DLogTNN)–based non-local hyperspectral image denoising model NL3DLogTNN is proposed. The correlation between the various modes of the model was obtained by performing TNN decomposition in three directions on the extracted non-local comparable blocks, better capturing the global low-rank property of the image. To effectively solve the proposed NL3DLogTNN model, we developed an approximate alternating direction method of multipliers (ADMM)-based methodology and offered a thorough numerical convergence proof. Extensive experiments are conducted on hyperspectral image datasets with simulated noise and real-world noise, which demonstrated that the proposed NL3DLogTNN model outperforms state-of-the-art methods in terms of quantitative and visual performance evaluation. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10214274/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3304005 |
| URL الوصول: |
https://doaj.org/article/62825c362752442dab0c9c5540088a1e |
| رقم الانضمام: |
edsdoj.62825c362752442dab0c9c5540088a1e |
| قاعدة البيانات: |
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