Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, 73-78, 2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
18 May 2015
D. N. H. Thanh and S. D. Dvoenko Tula State University, Institute of Applied Mathematics and Computer Sciences, Lenin ave. 92 Tula city, Russian Federation
Keywords: Total variation, ROF model, Gaussian noise, Poisson noise, Mixed Poisson-Gaussian noise, Image processing, Biomedical image, Euler-Lagrange equation Abstract. Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).
Conference paper (PDF, 896 KB)

Citation: Thanh, D. N. H. and Dvoenko, S. D.: A DENOISING OF BIOMEDICAL IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, 73-78, doi:10.5194/isprsarchives-XL-5-W6-73-2015, 2015.

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