Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal
In this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of the coherent imaging systems, which degrades the quality of the resulting images...
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Amirkabir University of Technology
2021-12-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_4324_6c928245035fb3f39006e6b3dc7d5498.pdf |
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| author | Zahra Hosseini Mohammadreza Hassannejad Bibalan |
| author_facet | Zahra Hosseini Mohammadreza Hassannejad Bibalan |
| author_sort | Zahra Hosseini |
| collection | DOAJ |
| description | In this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of the coherent imaging systems, which degrades the quality of the resulting images. Gaussian filter is a traditional approach for speckle denoising. However, estimating its optimum variance is still a challenge. Many algorithms have been developed to estimate the optimum variance, but they suffer from the type of noise or a predetermined variance. Our proposed method demonstrates an improved 2D Gaussian filter since it estimates the optimum variance of the filter in the context of differential entropy between the noisy and filtered images under different -norms. This optimum variance is directly estimated from the speckle noise level of image and it differs for different types of noise and images. The optimization problem is numerically solved, and the value of the norm order is also appropriately determined. The blind estimation of norm order is also accomplished based on the level of noise variance. Finally, the proposed method's performance is appraised, utilizing both standard and real ultrasound (US) images. The quality of filtered images is assessed through the qualitative and quantitative simulations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CoC), structural similarity (SSIM), and equivalent number of looks (ENL). The experimental results reveal the proposed method's proficiency in contradiction to state-of-the-art despeckling methods through the capability of strong speckle noise removal and preserving the edges and local features. |
| format | Article |
| id | doaj-art-99d8ef4fecb0472d93cd13bd3356bd84 |
| institution | OA Journals |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-99d8ef4fecb0472d93cd13bd3356bd842025-08-20T02:34:56ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292021-12-0153218920010.22060/eej.2021.19374.53894324Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise RemovalZahra Hosseini0Mohammadreza Hassannejad Bibalan1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of TechnologyDepartment of Electrical Engineering, Imam Khomeini International University, Qazvin, IranIn this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of the coherent imaging systems, which degrades the quality of the resulting images. Gaussian filter is a traditional approach for speckle denoising. However, estimating its optimum variance is still a challenge. Many algorithms have been developed to estimate the optimum variance, but they suffer from the type of noise or a predetermined variance. Our proposed method demonstrates an improved 2D Gaussian filter since it estimates the optimum variance of the filter in the context of differential entropy between the noisy and filtered images under different -norms. This optimum variance is directly estimated from the speckle noise level of image and it differs for different types of noise and images. The optimization problem is numerically solved, and the value of the norm order is also appropriately determined. The blind estimation of norm order is also accomplished based on the level of noise variance. Finally, the proposed method's performance is appraised, utilizing both standard and real ultrasound (US) images. The quality of filtered images is assessed through the qualitative and quantitative simulations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CoC), structural similarity (SSIM), and equivalent number of looks (ENL). The experimental results reveal the proposed method's proficiency in contradiction to state-of-the-art despeckling methods through the capability of strong speckle noise removal and preserving the edges and local features.https://eej.aut.ac.ir/article_4324_6c928245035fb3f39006e6b3dc7d5498.pdfimage denoising2d gaussian filterspeckle noiseentropy |
| spellingShingle | Zahra Hosseini Mohammadreza Hassannejad Bibalan Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal AUT Journal of Electrical Engineering image denoising 2d gaussian filter speckle noise entropy |
| title | Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal |
| title_full | Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal |
| title_fullStr | Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal |
| title_full_unstemmed | Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal |
| title_short | Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal |
| title_sort | entropy based parameter estimation of 2d gaussian filter for image speckle noise removal |
| topic | image denoising 2d gaussian filter speckle noise entropy |
| url | https://eej.aut.ac.ir/article_4324_6c928245035fb3f39006e6b3dc7d5498.pdf |
| work_keys_str_mv | AT zahrahosseini entropybasedparameterestimationof2dgaussianfilterforimagespecklenoiseremoval AT mohammadrezahassannejadbibalan entropybasedparameterestimationof2dgaussianfilterforimagespecklenoiseremoval |