Abstract
Although the traditional TV (Total Variation) model owns excellent image denoising ability, there are staircase effect problems for TV model. In this article, two detection operators for staircase effect problem are proposed. The staircase effect problem can be solved effectively by introducing two operators into traditional TV model. On the basis, it proposes an adaptive total variation model for image denoising. When dealing with image edge, it can still use the traditional TV model. Its purpose is to maintain the advantages in edge protection for TV model. When it is in the smooth area of image, linear diffusion is used to avoid the staircase effect.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. Pattern Anal Mach Intell 12(7):629–639
You Y, Kaveh M (2000) Fourth order partial differential equations for noise removal. Image Process 9(10):1723–1730
Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noiseremoval algorithms. Physica D 60:259–268
Xu J (2006) Iterative regularization and nonlinear inverse scale space methods in image restoration. University of California, Los Angeles
Kass M, Witkin A (1987) Analyzing oriented patterns. Comp Vision Graph Image Process 37:362–385
Knutsson H, Granlund GH (1983) Texture analysis using two-dimensional quadrature filters. In: IEEE computer society workshop on computer architecture for pattern analysis and image database management, pp 206–213
Knutsson H, Wilson R, Granlund GH (1983) Anisotropic nonstationary image estimation and its applications: Part 1–restoration of noisy images. IEEE Trans Commun 31(3):388–397
Zucker SW (1985) Early orientation selection: tangent fields and the dimensionality of their support. Comp Vision Graph Images Process 32:74–103
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906
Yokono JJ, Poggio T (2004) Oriented filters for object recognition: an empirical study. In: IEEE international conference on automatic face and gesture recognition processing, pp 755–760
Guo X, Wu R (2014) Application research on adaptive total variation image denoising. J Hebei North Univ (Nat Sci Ed) 30(5):21–24
Chan T, Esedpglu S, Park F et al (2005) Recent developments in total variation image restoration. Technique report, UCLA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Xiaoling, G., Jie, Y., Xiao, Z. (2016). An Algorithm for Image Denoising Based on Adaptive Total Variation. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-10-0539-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0538-1
Online ISBN: 978-981-10-0539-8
eBook Packages: Computer ScienceComputer Science (R0)