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Image Denoising Based on Overcomplete Topographic Sparse Coding

  • Haohua Zhao
  • Jun Luo
  • Zhiheng Huang
  • Takefumi Nagumo
  • Jun Murayama
  • Liqing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose sparseness constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves over previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, and shrinkage function. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.

Keywords

overcomplete sparse coding topograph image denoising multi-category adative learning shrinkage 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Haohua Zhao
    • 1
  • Jun Luo
    • 2
  • Zhiheng Huang
    • 1
  • Takefumi Nagumo
    • 2
  • Jun Murayama
    • 2
  • Liqing Zhang
    • 1
  1. 1.MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Dep. of Computer Science & EngineeringShanghai Jiao Tong Univ.ShanghaiChina
  2. 2.SONY CorporationJapan

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