Image Inpainting by Low-Rank Prior and Iterative Denoising
To reconstruct the missing or damaged parts of images from observed incomplete data, some traditional methods have been researched in recent years. The iterative denoising and backward projections(IDBP)algorithm with a simple parameter mechanism have been recently introduced, which solves the typica...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9133422/ |
| _version_ | 1818330143346655232 |
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| author | Ruyi Han Shumei Wang Shujun Fu Yuliang Li Shouyi Liu Weifeng Zhou |
| author_facet | Ruyi Han Shumei Wang Shujun Fu Yuliang Li Shouyi Liu Weifeng Zhou |
| author_sort | Ruyi Han |
| collection | DOAJ |
| description | To reconstruct the missing or damaged parts of images from observed incomplete data, some traditional methods have been researched in recent years. The iterative denoising and backward projections(IDBP)algorithm with a simple parameter mechanism have been recently introduced, which solves the typical inverse problem by utilizing the existing 3D transform-domain collaborative filtering denoising algorithm(BM3D). While this algorithm has simple parameter tuning, the collaborative hard-thresholding applied to the 3D group is greatly restricted in the procedure of denoising. In this paper, we remedy this deficiency using an iteration reweighted shrinkage denoising method. First, the model is obtained by a Plug and Play(P&P) framework. Then, we solve the optimization problem by using a proposed denoising model based on low rank prior and reweighted shrinkage and obtain a closed-form solution. Finally, the closed-form solution is operated iteratively by using the adaptive backward projection technique. Utilizing this novel strategy, the proposed algorithm not only removes the image noise and effectively recovers the degraded image, but also preserves fine structure and texture information of the image. Experimental results indicate that the proposed algorithm is competitive with some state-of-the-art inpainting algorithms in terms of both numerical evaluation and visual quality. |
| first_indexed | 2024-12-13T12:59:16Z |
| format | Article |
| id | doaj.art-66c0721666024c5e80a53d3333f9c272 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:59:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66c0721666024c5e80a53d3333f9c2722022-12-21T23:45:04ZengIEEEIEEE Access2169-35362020-01-01812331012331910.1109/ACCESS.2020.30072049133422Image Inpainting by Low-Rank Prior and Iterative DenoisingRuyi Han0https://orcid.org/0000-0003-4167-6069Shumei Wang1Shujun Fu2Yuliang Li3Shouyi Liu4https://orcid.org/0000-0002-7186-6667Weifeng Zhou5School of Mathematics, Shandong University, Jinan, ChinaThe First Department of General Surgery, Yidu Central Hospital of Weifang, Qingzhou, ChinaSchool of Mathematics, Shandong University, Jinan, ChinaDepartment of Intervention Medicine, The Second Hospital of Shandong University, Jinan, ChinaDepartment of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, ChinaTo reconstruct the missing or damaged parts of images from observed incomplete data, some traditional methods have been researched in recent years. The iterative denoising and backward projections(IDBP)algorithm with a simple parameter mechanism have been recently introduced, which solves the typical inverse problem by utilizing the existing 3D transform-domain collaborative filtering denoising algorithm(BM3D). While this algorithm has simple parameter tuning, the collaborative hard-thresholding applied to the 3D group is greatly restricted in the procedure of denoising. In this paper, we remedy this deficiency using an iteration reweighted shrinkage denoising method. First, the model is obtained by a Plug and Play(P&P) framework. Then, we solve the optimization problem by using a proposed denoising model based on low rank prior and reweighted shrinkage and obtain a closed-form solution. Finally, the closed-form solution is operated iteratively by using the adaptive backward projection technique. Utilizing this novel strategy, the proposed algorithm not only removes the image noise and effectively recovers the degraded image, but also preserves fine structure and texture information of the image. Experimental results indicate that the proposed algorithm is competitive with some state-of-the-art inpainting algorithms in terms of both numerical evaluation and visual quality.https://ieeexplore.ieee.org/document/9133422/Image inpaintingiterative denoisinginverse problemsingular value shrinkagelow-rank prior |
| spellingShingle | Ruyi Han Shumei Wang Shujun Fu Yuliang Li Shouyi Liu Weifeng Zhou Image Inpainting by Low-Rank Prior and Iterative Denoising IEEE Access Image inpainting iterative denoising inverse problem singular value shrinkage low-rank prior |
| title | Image Inpainting by Low-Rank Prior and Iterative Denoising |
| title_full | Image Inpainting by Low-Rank Prior and Iterative Denoising |
| title_fullStr | Image Inpainting by Low-Rank Prior and Iterative Denoising |
| title_full_unstemmed | Image Inpainting by Low-Rank Prior and Iterative Denoising |
| title_short | Image Inpainting by Low-Rank Prior and Iterative Denoising |
| title_sort | image inpainting by low rank prior and iterative denoising |
| topic | Image inpainting iterative denoising inverse problem singular value shrinkage low-rank prior |
| url | https://ieeexplore.ieee.org/document/9133422/ |
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