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This article is cited in 15 scientific papers (total in 15 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
Image noise removal based on total variation
D. N. Thanh, S. D. Dvoenko Tula State University
Abstract:
Today, raster images are created by different modern devices, such as digital cameras, X-Ray scanners, and so on. Image noise deteriorates the image quality, thus adversely affecting the result of processing. Biomedical images are an example of digital images. The noise in such raster images is assumed to be a mixture of Gaussian noise and Poisson noise. In this paper, we propose a method to remove these noises based on the total variation of the image brightness function. The proposed model is a combination of two famous denoising models, namely, the ROF model and a modified ROF model.
Keywords:
total variation, ROF model, Gaussian noise, Poisson noise, image processing, biomedical image, Euler-Lagrange equation.
Received: 14.07.2015 Revised: 28.07.2015
Citation:
D. N. Thanh, S. D. Dvoenko, “Image noise removal based on total variation”, Computer Optics, 39:4 (2015), 564–571
Linking options:
https://www.mathnet.ru/eng/co18 https://www.mathnet.ru/eng/co/v39/i4/p564
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Abstract page: | 397 | Full-text PDF : | 154 | References: | 45 |
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