Image denoising using local adaptive layered Wiener filter in the gradient domain

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• 作者：Xiaobo Zhang ; Xiangchu Feng
• 关键词：Wiener filter ; Local window ; Gradient domain ; Layered denoising ; Anisotropic diffusion
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：74
• 期：23
• 页码：10495-10514
• 全文大小：1,941 KB
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• 作者单位：Xiaobo Zhang (1)
  Xiangchu Feng (2)
  
  1. Institute of Graphics and Image Processing, Xianyang Normal University, Xianyang, 712000, China
  2. Department of Mathematics, Xidian University, Xi’an, 710071, China
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

This paper presents a new image denoising algorithm. Our method is inspired by locally adaptive window-based denoising using maximum likelihood (LAWML). In the research, we find, as with wavelet coefficients, the gradient image coefficients can also be modeled as zero-mean Gaussian random variables with high local correlation. So, we implement the local adaptive Wiener filter in the gradient domain. But unlike LAWML, the layered denoising is adopted in our method. At the same time, the relation between wavelet-based and diffusion-based denoising method is disclosed further. The tests demonstrate the proposed method gets the desired results both subjectively and objectively compared to the related gradient domain algorithms and wavelet-based image denoising methods. At the same time, the tests also show the proposed method outperforms some other diffusion filters and wavelet-based methods and Non-Local means (NL-means) filter in most cases.