Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion
Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does no...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1706 |
| _version_ | 1797623129977126912 |
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| author | Xiaohua Liu Guijin Tang |
| author_facet | Xiaohua Liu Guijin Tang |
| author_sort | Xiaohua Liu |
| collection | DOAJ |
| description | Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does not have a strong low-rank property. In order to enhance the low-rank property, we propose a novel method called sub-image based low-rank tensor completion (SLRTC) for image restoration. We first sample a color image to obtain sub-images, and adopt these sub-images instead of the original single image to form a tensor. Then we conduct the mode permutation on this tensor. Next, we exploit the tensor nuclear norm defined based on the tensor-singular value decomposition (t-SVD) to build the low-rank completion model. Finally, we perform the tensor-singular value thresholding (t-SVT) based the standard alternating direction method of multipliers (ADMM) algorithm to solve the aforementioned model. Experimental results have shown that compared with the state-of-the-art tensor completion techniques, the proposed method can provide superior results in terms of objective and subjective assessment. |
| first_indexed | 2024-03-11T09:24:19Z |
| format | Article |
| id | doaj.art-94323b5471d84b04b454bdea670ebc37 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:24:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-94323b5471d84b04b454bdea670ebc372023-11-16T18:04:59ZengMDPI AGSensors1424-82202023-02-01233170610.3390/s23031706Color Image Restoration Using Sub-Image Based Low-Rank Tensor CompletionXiaohua Liu0Guijin Tang1College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaJiangsu Key Laboratory of Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaMany restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does not have a strong low-rank property. In order to enhance the low-rank property, we propose a novel method called sub-image based low-rank tensor completion (SLRTC) for image restoration. We first sample a color image to obtain sub-images, and adopt these sub-images instead of the original single image to form a tensor. Then we conduct the mode permutation on this tensor. Next, we exploit the tensor nuclear norm defined based on the tensor-singular value decomposition (t-SVD) to build the low-rank completion model. Finally, we perform the tensor-singular value thresholding (t-SVT) based the standard alternating direction method of multipliers (ADMM) algorithm to solve the aforementioned model. Experimental results have shown that compared with the state-of-the-art tensor completion techniques, the proposed method can provide superior results in terms of objective and subjective assessment.https://www.mdpi.com/1424-8220/23/3/1706sub-imagelow ranktensor completionimage restoration |
| spellingShingle | Xiaohua Liu Guijin Tang Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion Sensors sub-image low rank tensor completion image restoration |
| title | Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion |
| title_full | Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion |
| title_fullStr | Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion |
| title_full_unstemmed | Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion |
| title_short | Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion |
| title_sort | color image restoration using sub image based low rank tensor completion |
| topic | sub-image low rank tensor completion image restoration |
| url | https://www.mdpi.com/1424-8220/23/3/1706 |
| work_keys_str_mv | AT xiaohualiu colorimagerestorationusingsubimagebasedlowranktensorcompletion AT guijintang colorimagerestorationusingsubimagebasedlowranktensorcompletion |