Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization
Abstract Most of the information obtained by humans comes from colour images. However, salt‐and‐pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12680 |
| _version_ | 1797903379452657664 |
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| author | Jun Zhang Zhao‐yang Li Ling‐zhi Wang Ying‐pin Chen |
| author_facet | Jun Zhang Zhao‐yang Li Ling‐zhi Wang Ying‐pin Chen |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | Abstract Most of the information obtained by humans comes from colour images. However, salt‐and‐pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into three independent matrices according to the colour channel and then recover each channel signal independently, ignoring the strong data correlation between channels. In addition, most existing SPN denoising methods apply only a single model‐driven or data‐driven approach and fail to take the advantages of their combination fully. Therefore, we first regard a colour image contaminated by SPN as the sum of an SPN tensor and a tensor with missing data. In this manner, we transform the denoising problem into a low‐rank tensor reconstruction problem. We then introduce a model‐driven‐based parallel matrix factorization low‐rank tensor reconstruction algorithm and a data‐driven‐based FFDNet denoising network to restore the colour image better. The proposed method not only enhances the similarity of the colour image channels but also explores the deep prior of the colour image to capture the image details. Finally, the proposed method is compared with some advanced denoising methods. The results show that the proposed method achieves a competitive denoising performance. |
| first_indexed | 2024-04-10T09:31:58Z |
| format | Article |
| id | doaj.art-67d6bdcc1ef8450988d98fa7addb37f0 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-10T09:31:58Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-67d6bdcc1ef8450988d98fa7addb37f02023-02-19T04:18:32ZengWileyIET Image Processing1751-96591751-96672023-02-0117388690010.1049/ipr2.12680Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularizationJun Zhang0Zhao‐yang Li1Ling‐zhi Wang2Ying‐pin Chen3School of Physics and Information Engineering Minnan Normal University Zhangzhou ChinaSchool of Physics and Information Engineering Minnan Normal University Zhangzhou ChinaDepartment of Electronic and Information Engineering Xiamen City University Xiamen ChinaSchool of Physics and Information Engineering Minnan Normal University Zhangzhou ChinaAbstract Most of the information obtained by humans comes from colour images. However, salt‐and‐pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into three independent matrices according to the colour channel and then recover each channel signal independently, ignoring the strong data correlation between channels. In addition, most existing SPN denoising methods apply only a single model‐driven or data‐driven approach and fail to take the advantages of their combination fully. Therefore, we first regard a colour image contaminated by SPN as the sum of an SPN tensor and a tensor with missing data. In this manner, we transform the denoising problem into a low‐rank tensor reconstruction problem. We then introduce a model‐driven‐based parallel matrix factorization low‐rank tensor reconstruction algorithm and a data‐driven‐based FFDNet denoising network to restore the colour image better. The proposed method not only enhances the similarity of the colour image channels but also explores the deep prior of the colour image to capture the image details. Finally, the proposed method is compared with some advanced denoising methods. The results show that the proposed method achieves a competitive denoising performance.https://doi.org/10.1049/ipr2.12680data‐drivenFFDNetmodel‐drivenparallel matrix factorizationsalt and pepper denoising |
| spellingShingle | Jun Zhang Zhao‐yang Li Ling‐zhi Wang Ying‐pin Chen Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization IET Image Processing data‐driven FFDNet model‐driven parallel matrix factorization salt and pepper denoising |
| title | Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization |
| title_full | Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization |
| title_fullStr | Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization |
| title_full_unstemmed | Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization |
| title_short | Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization |
| title_sort | salt and pepper denoising method for colour images based on tensor low rank prior and implicit regularization |
| topic | data‐driven FFDNet model‐driven parallel matrix factorization salt and pepper denoising |
| url | https://doi.org/10.1049/ipr2.12680 |
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