Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-06

AUTHORS

Haohua Zhao, Jun Luo, Zhiheng Huang, Takefumi Nagumo, Jun Murayama, Liqing Zhang

ABSTRACT

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose both spareseness and topograpgic constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves the previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, lasso shrinkage function, and subset selection. 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. More... »

PAGES

357-369

References to SciGraph publications

  • 2013. Image Denoising Based on Overcomplete Topographic Sparse Coding in NEURAL INFORMATION PROCESSING
  • 2011. Sparse Coding Image Denoising Based on Saliency Map Weight in NEURAL INFORMATION PROCESSING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11063-014-9384-3

    DOI

    http://dx.doi.org/10.1007/s11063-014-9384-3

    DIMENSIONS

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