| dc.creator | Forero, Manuel G. | |
| dc.creator | Miranda, Sergio L. | |
| dc.creator | Jacanamejoy-Jamioy, Carlos | |
| dc.date | 2020-09-16T15:01:31Z | |
| dc.date | 2020-09-16T15:01:31Z | |
| dc.date | 2020-06-24 | |
| dc.date.accessioned | 2023-08-31T19:04:10Z | |
| dc.date.available | 2023-08-31T19:04:10Z | |
| dc.identifier | Forero, M.G., Miranda, S.L., & Jacanamejoy-Jamioy, C.A. (2020). Improvement of the Turajli? Method for the Estimation of Gaussian Noise in Images. Pattern Recognition, 12088, 108 - 117. | |
| dc.identifier | 0302-9743 | |
| dc.identifier | https://link.springer.com/chapter/10.1007/978-3-030-49076-8_11 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8555168 | |
| dc.description | Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajli? was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost. | |
| dc.description | Universidad de Ibagu? | |
| dc.language | en | |
| dc.publisher | Lecture notes in computer sciences | |
| dc.subject | Noise estimation | |
| dc.subject | Gaussian noise | |
| dc.subject | Image filtering | |
| dc.subject | Smoothing filters | |
| dc.subject | Noise reduction | |
| dc.title | Improvement of the Turajli? Method for the Estimation of Gaussian Noise in Images | |
| dc.type | Article | |
