Adaptive total variation denoising based on difference curvature

  • Authors:

  • Qiang Chen;Philippe Montesinos;Quan Sen Sun;Peng Ann Heng;De Shen Xia

  • Affiliations:

  • The School of Computer Science and Technology, Nanjing University of Science and Technology, Xiao Lingwei 200#, Nanjing, Jiangsu 210094, China;EMA-EERIE, LGI2P, Parc Scientifique G. Besse, Nimes, France;The School of Computer Science and Technology, Nanjing University of Science and Technology, Xiao Lingwei 200#, Nanjing, Jiangsu 210094, China;Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, The Chinese University of Hong Kong, Shenzhen, China and Department of Computer Science & Engineering, The Chine ...;The School of Computer Science and Technology, Nanjing University of Science and Technology, Xiao Lingwei 200#, Nanjing, Jiangsu 210094, China

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2010

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Abstract

Image denoising methods based on gradient dependent regularizers such as Rudin et al.'s total variation (TV) model often suffer the staircase effect and the loss of fine details. In order to overcome such drawbacks, this paper presents an adaptive total variation method based on a new edge indicator, named difference curvature, which can effectively distinguish between edges and ramps. With adaptive regularization and fidelity terms, the new model has the following properties: at object edges, the regularization term is approximate to the TV norm in order to preserve the edges, and the weight of the fidelity term is large in order to preserve details; in flat and ramp regions, the regularization term is approximate to the L2 norm in order to avoid the staircase effect, and the weight of the fidelity term is small in order to strongly remove the noise. Comparative results on both synthetic and natural images demonstrate that the new method can avoid the staircase effect and better preserve fine details.