An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics
DOI:
https://doi.org/10.5566/ias.2703Keywords:
image denoising, neighboring coefficients, wavelet transformsAbstract
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.
References
Alessandro M., Juan M. H., Mercedes E. P. et al. (2020). A Single Model CNN for Hyperspectral Image Denoising. IEEE Trans. Geosci. Remote Sens. 58(4):2516-29.
Cai T. T., Silverman B. W. (2001). Incorporating information on neighboring coefficients into wavelet estimation. Sankhya: Ind. J. Stat. B, pt. 2 63:127-48.
Çelik T., Özkaramanlı H., DemirelH. (2008). Facial feature extraction using complex dual-tree wavelet transform. Comput. Vision Image Understanding 111:229-46.
Chang S.G., Yu B., Vetterli M. (2000). Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans. Image Process. 9(9):1522-31.
Chen G. Y., Bui T. D. (2003). Multiwavelets Denoising Using Neighboring Coefficients. IEEE Signal Process Lett. 10(7):211-14.
Chen G.Y., Bui T.D., Krzyzak A. (2005). Image denoising with neighbour dependency and customized wavelet and threshold. Pattern Recognit. 38:115-24.
Chen Y. and Han C. (2005). Adaptive wavelet threshold for image denoising. Electron. Lett. 41(10):586-87.
Choi H., Baraniuk R.G. (1998). Analysis of Wavelet-Domain Wiener Filters. the IEEE-SP Int. Sym. on Time-frequency and Time-scale Analysis 613-16.
Choi H., Baraniuk R.G. (2004). Multiple wavelet basis image denoising using Besov ball projections. IEEE Signal Process Lett. 11(9):717-20.
Coifman R. R., Dohono D. L. (1995). Translation-invariant de-noising. in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, Eds., New York, Springer-Verlag 125-50.
Dohono D.L. (1995). De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3):613–27.
Dohono D.L., Johnstone I.M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425-55.
Dohono D.L., Johnstone I.M. (1995). Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc. 90:1200-24.
Fadi B., Naser D., Florian K., Arjan K. (2022). Self-restrained triplet loss for accurate masked face recognition. Pattern Recognit. 124:108473.
Ghael S., Sayeed A., Baraniuk R. (1997). Improved wavelet denoising via empirical wiener filtering. Proc. SPIE, San Diego 3169:389-99.
Gonzalez R.C., Woods R.E. (2002). Digital Image Processing. 2nd ed. Prentice-Hall, Englewood CliffsNew Jersey,
Lalith K. S. S., Otto M., Irène B., Luc B., Thomas B. (2021). Potentials and caveats of AI in hybrid imaging. Methods, 188:4-19.
Mallat S. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11:674-93.
Sendur L., Selesnick I.W. (2002a). Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50(11):2744-56.
Sendur L., Selesnick I. W. (2002b). Bivariate Shrinkage with Local Variance Estimation. IEEE Signal Process Lett. 9(12):438-41.
Shengqian W., Yuanhua Z., Daowen Z. (2002). Adaptive shrinkage de-noising using neighborhood characteristic. Electron. Lett. 38(17):502-3.
Simoncelli E.P., Adelson E.H. (1996). Noise removal via Bayesian wavelet coring. The 3rd Int. Conf. on Image Process., Lausanne, Switzerland 1:379-82.
Ying Y., Yusen W. (2010). Random Interpolation Average for Signal Denoising. Signal Process., IET. 4(6):708-19.
Ying Y., Yusen W. (2013). Random interpolation average for ECG signal denoising using multiple wavelet bases. Biomed. Eng. Appl. Basis Commun. 25(4), 1350042.
Yu-Hsuan H., Homer H. C. (2022). Deep face recognition for dim images. Pattern Recognit. 126:108580.
Zhou D., Cheng W. (2008). Image denoising with an optimal threshold and neighbouring window. Pattern Recognit. Lett. 29:1694-97.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Image Analysis & Stereology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
