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Learning Non-Negativity Constrained Variation for Image Denoising and Deblurring

  • Tengda Wei (a1), Linshan Wang (a2), Ping Lin (a3), Jialing Chen (a3), Yangfan Wang (a4) and Haiyong Zheng (a5)...
Abstract
Abstract

This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning strategy. The model includes two terms: a problem-based term that is derived from the prior knowledge, and an image-driven regularization which is learned by some training samples. The model can be solved by classical ε-constraint method. Experimental results show that: the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof; the proposed method outperforms other PDE-based methods on image denoising and deblurring.

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Corresponding author
*Corresponding author. Email addresses: tdwei123@163.com (T. Wei), wangls@ouc.edu.cn (L. Wang), plin@maths.dundee.ac.uk (P. Lin), j.z.chen@dundee.ac.uk (J. Chen), yfwang@ouc.edu.cn (Y. Wang), zhenghaiyong@ouc.edu.cn (H. Zheng)
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Numerical Mathematics: Theory, Methods and Applications
  • ISSN: 1004-8979
  • EISSN: 2079-7338
  • URL: /core/journals/numerical-mathematics-theory-methods-and-applications
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