Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition
Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-ra...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/8/1343 |
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| author | Zhihao Liu Weiqi Jin Li Li |
| author_facet | Zhihao Liu Weiqi Jin Li Li |
| author_sort | Zhihao Liu |
| collection | DOAJ |
| description | Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. Additionally, the Laplacian operator-based bidirectional twisted tensor truncated nuclear norm (bt-LPTNN), is proposed, which is a norm that automatically assigns weights to different singular values based on their importance. Furthermore, a weighted spatiotemporal total variation regularization method for nonconvex tensor approximation is employed to preserve scene details. To recover spatial domain information lost during tensor estimation, robust principal component analysis is employed, and spatial information is extracted from the noise tensor. The proposed model, bt-LPTVTD, is solved using an augmented Lagrange multiplier algorithm, which outperforms several state-of-the-art algorithms. Compared to some of the latest algorithms, bt-LPTVTD demonstrates improvements across all evaluation metrics. Extensive experiments conducted using complex scenes underscore the strong adaptability and robustness of our algorithm. |
| format | Article |
| id | doaj-art-91a548714f9c402c8461916f7c330d4e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-91a548714f9c402c8461916f7c330d4e2025-08-20T02:28:28ZengMDPI AGRemote Sensing2072-42922025-04-01178134310.3390/rs17081343Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor DecompositionZhihao Liu0Weiqi Jin1Li Li2MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, ChinaMOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, ChinaMOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, ChinaTemporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. Additionally, the Laplacian operator-based bidirectional twisted tensor truncated nuclear norm (bt-LPTNN), is proposed, which is a norm that automatically assigns weights to different singular values based on their importance. Furthermore, a weighted spatiotemporal total variation regularization method for nonconvex tensor approximation is employed to preserve scene details. To recover spatial domain information lost during tensor estimation, robust principal component analysis is employed, and spatial information is extracted from the noise tensor. The proposed model, bt-LPTVTD, is solved using an augmented Lagrange multiplier algorithm, which outperforms several state-of-the-art algorithms. Compared to some of the latest algorithms, bt-LPTVTD demonstrates improvements across all evaluation metrics. Extensive experiments conducted using complex scenes underscore the strong adaptability and robustness of our algorithm.https://www.mdpi.com/2072-4292/17/8/1343infrared focal plane arraytemporal random noisenonconvex tensor low-ranktensor truncated nuclear norm |
| spellingShingle | Zhihao Liu Weiqi Jin Li Li Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition Remote Sensing infrared focal plane array temporal random noise nonconvex tensor low-rank tensor truncated nuclear norm |
| title | Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition |
| title_full | Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition |
| title_fullStr | Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition |
| title_full_unstemmed | Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition |
| title_short | Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition |
| title_sort | temporal denoising of infrared images via total variation and low rank bidirectional twisted tensor decomposition |
| topic | infrared focal plane array temporal random noise nonconvex tensor low-rank tensor truncated nuclear norm |
| url | https://www.mdpi.com/2072-4292/17/8/1343 |
| work_keys_str_mv | AT zhihaoliu temporaldenoisingofinfraredimagesviatotalvariationandlowrankbidirectionaltwistedtensordecomposition AT weiqijin temporaldenoisingofinfraredimagesviatotalvariationandlowrankbidirectionaltwistedtensordecomposition AT lili temporaldenoisingofinfraredimagesviatotalvariationandlowrankbidirectionaltwistedtensordecomposition |