Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images
Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SV...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8540808/ |
| _version_ | 1818415520918011904 |
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| author | Emir Turajlic |
| author_facet | Emir Turajlic |
| author_sort | Emir Turajlic |
| collection | DOAJ |
| description | Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level <inline-formula> <tex-math notation="LaTeX">$\sigma = 15$ </tex-math></inline-formula>, the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time. |
| first_indexed | 2024-12-14T11:36:18Z |
| format | Article |
| id | doaj.art-4c9d43863dfb4d8cbd3830b79013d325 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:36:18Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4c9d43863dfb4d8cbd3830b79013d3252022-12-21T23:03:02ZengIEEEIEEE Access2169-35362018-01-016727357274710.1109/ACCESS.2018.28822988540808Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in ImagesEmir Turajlic0https://orcid.org/0000-0003-0274-660XDepartment of Telecommunications, University of Sarajevo, Sarajevo, Bosnia and HerzegovinaNoise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level <inline-formula> <tex-math notation="LaTeX">$\sigma = 15$ </tex-math></inline-formula>, the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.https://ieeexplore.ieee.org/document/8540808/Digital imagesAWGNimage analysisleast square methodsartificial neural networksnoise level estimation |
| spellingShingle | Emir Turajlic Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images IEEE Access Digital images AWGN image analysis least square methods artificial neural networks noise level estimation |
| title | Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images |
| title_full | Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images |
| title_fullStr | Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images |
| title_full_unstemmed | Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images |
| title_short | Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images |
| title_sort | adaptive svd domain based white gaussian noise level estimation in images |
| topic | Digital images AWGN image analysis least square methods artificial neural networks noise level estimation |
| url | https://ieeexplore.ieee.org/document/8540808/ |
| work_keys_str_mv | AT emirturajlic adaptivesvddomainbasedwhitegaussiannoiselevelestimationinimages |