Abstract
Image denoising by minimizing a similarity of neighborhood-based cost function is presented. This cost function consists of two parts, one related to data fidelity and the other is a structure preserving smoothing term. The latter is controlled by a weight coefficient that measures the neighborhood similarity between two pixels and attaching an additional term penalizes it. Unlike most work in noise removal area, the weight of each pixel within the neighborhood is not defined by a Gaussian function. The obtained results show a good performance of our proposal, compared with some state-of-the-art algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tomasi, C., Manduchi, R.: Bilateral Filtering for Gray and Color Images. In: ICCV, pp. 839–846 (1998)
Buades, A., Coll, B., Morel, J.: A non local algorithm for image denoising. In: Proc. Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)
Astola, J., Kuosmanen, P.: Fundamentals of Nonlinear Digital Filtering. CRC Press, USA (1997)
Hwang, H., Haddad, R.A.: Adaptive median filters: new algorithms and results. IEEE Trans. On Image Processing 4(4), 499–502 (1995)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Rivera, M., Marroquin, J.L.: Adaptive rest potentials: first and second order edge-preserving regularization. Journal of Computer Vision and Image Understanding 88, 76–93 (2002)
Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Deterministic edge-preserving regularization in computed imaging. IEEE Trans. On Image Processing 6(2), 298–311 (1997)
Chan, R.H., Ho, C.W., Nikolova, M.: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. On Image Processing 14(10), 1479–1485 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calderon, F., Júnez–Ferreira, C.A. (2011). Regularization with Adaptive Neighborhood Condition for Image Denoising. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-25330-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25329-4
Online ISBN: 978-3-642-25330-0
eBook Packages: Computer ScienceComputer Science (R0)