Image Denoising Based on Overcomplete Topographic Sparse Coding View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

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 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. More... »

PAGES

266-273

References to SciGraph publications

  • 2011. Sparse Coding Image Denoising Based on Saliency Map Weight in NEURAL INFORMATION PROCESSING
  • Book

    TITLE

    Neural Information Processing

    ISBN

    978-3-642-42050-4
    978-3-642-42051-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-42051-1_34

    DOI

    http://dx.doi.org/10.1007/978-3-642-42051-1_34

    DIMENSIONS

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