A Content-Aware Non-Local Means Method for Image Denoising
Parameter setting and information redundancy are essential issues for the non-local means (NLM) algorithm. This paper introduces a new factor based on the Hessian matrix to adapt the smoothing parameter. Then, a strategy is proposed to implement the NLM by representing patches in terms of features,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/18/2898 |
| _version_ | 1797489031029719040 |
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| author | Shun Fang Jiaxin Wu Shiqian Wu |
| author_facet | Shun Fang Jiaxin Wu Shiqian Wu |
| author_sort | Shun Fang |
| collection | DOAJ |
| description | Parameter setting and information redundancy are essential issues for the non-local means (NLM) algorithm. This paper introduces a new factor based on the Hessian matrix to adapt the smoothing parameter. Then, a strategy is proposed to implement the NLM by representing patches in terms of features, which uses the 2D histogram and summed-area table. Compared with other methods, the metric for patch similarity in this paper is based on statistical features of patches instead of Euclidean distance. More importantly, not many predefined thresholds are needed. Experimental results show that the proposed algorithm obtains better visual quality and numerical results, especially for images with rich contents and high noise. |
| first_indexed | 2024-03-10T00:11:41Z |
| format | Article |
| id | doaj.art-033945a355cd41698eb53e826a35ab06 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:11:41Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-033945a355cd41698eb53e826a35ab062023-11-23T15:58:29ZengMDPI AGElectronics2079-92922022-09-011118289810.3390/electronics11182898A Content-Aware Non-Local Means Method for Image DenoisingShun Fang0Jiaxin Wu1Shiqian Wu2Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaInstitute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaInstitute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaParameter setting and information redundancy are essential issues for the non-local means (NLM) algorithm. This paper introduces a new factor based on the Hessian matrix to adapt the smoothing parameter. Then, a strategy is proposed to implement the NLM by representing patches in terms of features, which uses the 2D histogram and summed-area table. Compared with other methods, the metric for patch similarity in this paper is based on statistical features of patches instead of Euclidean distance. More importantly, not many predefined thresholds are needed. Experimental results show that the proposed algorithm obtains better visual quality and numerical results, especially for images with rich contents and high noise.https://www.mdpi.com/2079-9292/11/18/2898image denoisingnon-local meanscontent-awaresmoothing parameter |
| spellingShingle | Shun Fang Jiaxin Wu Shiqian Wu A Content-Aware Non-Local Means Method for Image Denoising Electronics image denoising non-local means content-aware smoothing parameter |
| title | A Content-Aware Non-Local Means Method for Image Denoising |
| title_full | A Content-Aware Non-Local Means Method for Image Denoising |
| title_fullStr | A Content-Aware Non-Local Means Method for Image Denoising |
| title_full_unstemmed | A Content-Aware Non-Local Means Method for Image Denoising |
| title_short | A Content-Aware Non-Local Means Method for Image Denoising |
| title_sort | content aware non local means method for image denoising |
| topic | image denoising non-local means content-aware smoothing parameter |
| url | https://www.mdpi.com/2079-9292/11/18/2898 |
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