Extended Shearlet HMT Model-Based Image Denoising Using BKF Distribution

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• 作者：Xiang-Yang Wang ; Na Zhang ; Hong-Liang Zheng…
• 关键词：Image denoising ; Hidden Markov tree (HMT) ; Extended discrete Shearlet transform (extended DST) ; Bessel K Form (BKF) distribution
• 刊名：Journal of Mathematical Imaging and Vision
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：54
• 期：3
• 页码：301-319
• 全文大小：3,194 KB
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• 作者单位：Xiang-Yang Wang (1)
  Na Zhang (1)
  Hong-Liang Zheng (1)
  Yang-Cheng Liu (1)
  
  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics
  Image Processing and Computer Vision
  Artificial Intelligence and Robotics
  Automation and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-7683

文摘

Images are often corrupted by noise in the procedures of image acquisition and transmission. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can represent images with very few coefficients. In this paper, we propose a new image denoising approach in extended DST domain, which combines hidden Markov tree (HMT) model and Bessel K Form (BKF) distribution. Firstly, the marginal statistics of extended DST coefficients are studied, and their distribution is analytically calculated by modeling extended DST coefficients with BKF probability density function. Then, an extended Shearlet HMT model is established for capturing the intra-scale, inter-scale, and cross-orientation coefficients dependencies. Finally, an image denoising approach based on the extended Shearlet HMT model is presented. Extensive experimental results demonstrate that our extended Shearlet HMT denoising approach can obtain better performances in terms of both subjective and objective evaluations than other state-of-the-art HMT denoising techniques. Especially, the proposed approach can preserve edges very well while removing noise.