An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics


  • Ying Yang Department of Electronic Engineering, Xi An University of Technology
  • Yusen Wei Department of Electronic Engineering, Xi an University of technology Xi An Vocational University of Automobile



image denoising, neighboring coefficients, wavelet transforms


The shrinkage function has an important effect on the image denoising results. An adaptive shrinkage function is developed in this paper to shrink the small coefficients properly for image denoising based on neighborhood characteristics. The shrinkage function is determined by the number of large coefficients near the current signal coefficients. In this way, different shrinkage functions can be adaptively used to deal with different coefficients in the process of image denoising, instead of using fixed shrinkage functions. Experimental results show that the SNR of the image processed by the adaptive shrink function algorithm is better than that processed by the soft threshold, hard threshold, and neighborhood shrink algorithm. Moreover, compared with the traditional soft threshold, hard threshold and neighborhood shrink algorithm, the PSNR of the algorithm using adaptive shrink function increases by 3.68dB, 2.28dB and 0.61dB, respectively. In addition, the proposed new algorithms, soft threshold and hard threshold, are combined with empirical Wiener filtering and shift invariant (TI) scheme to compare their image noise reduction effects. The results show that the PSNR can be improved significantly by using the adaptive shrink function algorithm combined with empirical Wiener filtering and shift invariant (TI) scheme. 

Author Biography

Ying Yang, Department of Electronic Engineering, Xi An University of Technology

Corresponding Author

She is an Assocaite Professor at Xi An University of Technology 


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How to Cite

Yang, Y., & Wei, Y. (2022). An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics. Image Analysis and Stereology, 41(2), 121–131.



Original Research Paper