Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/4/1019 |
| _version_ | 1811302376628289536 |
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| author | Sangyoon Lee Min Seok Lee Moon Gi Kang |
| author_facet | Sangyoon Lee Min Seok Lee Moon Gi Kang |
| author_sort | Sangyoon Lee |
| collection | DOAJ |
| description | The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods. |
| first_indexed | 2024-04-13T07:27:15Z |
| format | Article |
| id | doaj.art-a16dca9e89de4ede96b86c2443f31cb0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:27:15Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a16dca9e89de4ede96b86c2443f31cb02022-12-22T02:56:27ZengMDPI AGSensors1424-82202018-03-01184101910.3390/s18041019s18041019Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT DomainSangyoon Lee0Min Seok Lee1Moon Gi Kang2School of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, KoreaSchool of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, KoreaSchool of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, KoreaThe noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.http://www.mdpi.com/1424-8220/18/4/1019low-dose X-raynon-subsampled contourlet transform (NSCT)Poisson–Gaussian noisenoise analysisnoise estimation |
| spellingShingle | Sangyoon Lee Min Seok Lee Moon Gi Kang Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain Sensors low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation |
| title | Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain |
| title_full | Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain |
| title_fullStr | Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain |
| title_full_unstemmed | Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain |
| title_short | Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain |
| title_sort | poisson gaussian noise analysis and estimation for low dose x ray images in the nsct domain |
| topic | low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation |
| url | http://www.mdpi.com/1424-8220/18/4/1019 |
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