Gradient-based Wiener filter for image denoising

Authors:
Xiaobo Zhang;Xiangchu Feng;Weiwei Wang;Shunli Zhang;Qunfeng Dong
Affiliations:
Department of Mathematics, Xidian University, Xi'an 710071, China and Institute of Graphics and Image Processing, Xianyang Normal University, Xianyang 712000, China;Department of Mathematics, Xidian University, Xi'an 710071, China;Department of Mathematics, Xidian University, Xi'an 710071, China;Institute of Graphics and Image Processing, Xianyang Normal University, Xianyang 712000, China;Institute of Graphics and Image Processing, Xianyang Normal University, Xianyang 712000, China
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

In this paper, we develop a new adaptive image denoising algorithm in the presence of Gaussian noise. Because the proposed method operates in the gradient domain and is close to Wiener filter, it is named as gradient-based Wiener filter (GWF). Inspired by the Perona-Malik anisotropic diffusion (PMAD), the proposed algorithm is implemented by iterations. The parameters for the GWF are studied in full detail. At the same time, the tuning method of the gradient thresholding based on noise variance for PMAD is presented. Experimental results indicate the proposed algorithm achieves higher peak signal-to-noise ratio (PSNR) and better visual effect compared to related algorithms. On the other hand, the simulation results also show the tremendous power of the given parameter tuning method for PMAD.