Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (11): 260-266.doi: 10.11896/jsjkx.190400159

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Image Denoising Algorithm Based on Fast and Adaptive Bidimensional Empirical Mode Decomposition

LIU Pei1, JIA Jian1,2, CHEN Li1, AN Ying1   

  1. (School of Information Science and Technology,Northwest University,Xi’an 710127,China)1
    (School of Mathematics,Northwest University,Xi’an 710127,China)2
  • Received:2019-04-29 Online:2019-11-15 Published:2019-11-14

PDF (PC)

1230

Abstract: In order to adaptively decompose the image and accurately describe the distribution state of the decomposition coefficients,a new image denoising algorithm based on fast and adaptive bidimensional empirical mode decomposition algorithm was proposed.Firstly,the algorithm performs fast and adaptive bidimensional empirical mode decomposition on the image.By determining the number of noise-dominated subband after decomposition,the noise-dominated subband coefficient distribution is further modeled by the normal inverse Gaussian model.Then the Bayesian maximum posteriori probability estimation theory is used to derive the corresponding threshold from the model.Finally,the optimal linear interpolation threshold function algorithm is used to complete the denoising.The simulation results show that for adding Gaussian white noise images of different standard deviation,the average signal-to-noise ratio is improved by 4.36dB,0.85dB,0.78dB and 0.48dB,respectively,compared with sym4 wavelet denoising,bivariate threshold denoising,pro-ximity algorithms for total variation,and overlapping group sparse total variation algorithm.Structural similarity index is also improved with different degrees,which shows it can effectively preserve more image details.The experimental results show that the proposed algorithm is superior to the comparison algorithms in terms of visual performance and evaluation index.

Key words: Bayesian maximum posterior probability estimation theory, Fast and adaptive bidimensional empirical mode decomposition, Image de-noising, Normal inverse Gaussian model, OLI-Shrink threshold value

CLC Number: 

  • TP391.4
[1]SUN X X,QU W.Comparison between mean filter and median filter algorithm in image denoising field[C]∥Applied Mechanics and Materials.Trans Tech Publications,2014,644:4112-4116.
[2]PUSHPAVALLI R,SRINIVASAN E.Decision based switching median filtering technique for image denoising[J].CiiT International Journal of Digital Image Processing,2010,2(10):405-410.
[3]ABHARI K,MARSOUSI M,BABYN P,et al.Medical image denoising using low pass filtering in sparse domain[C]∥2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.NJ:IEEE,2012:114-117.
[4]BUADES A,COLL B,MOREL J M.A non-local algorithm for image denoising[C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).Washington DC:IEEE,2005,2:60-65.
[5]DABOV K,FOI A,KATKOVNIK V,et al.BM3D image denoising with shape-adaptive principal component analysis[C]∥Signal Processing with Adaptive Sparse Structured Representations.Saint Malo,France,2009.
[6]MALLAT S.A wavelet tour of signal processing[M].Elsevier,1999.
[7]RAN X Y,YE J Y,GUO C H.Image denoising method based on two-dimensional empirical mode and mean filtering[J].Journal of Computer Applications,2008,28(11):2884-2886.
[8]ABHARI K,MARSOUSI M,BABYN P,et al.Medical image denoising using low pass filtering in sparse domain∥2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Piscataway,NJ:IEEE Press,2012:114-117.
[9]ARFIA F B,SABRI A,MESSAOUD M B,et al.The bidimensional empirical mode decomposition with 2D-DWT for gaussian image denoising[C]∥2011 17th International Conference on Digital Signal Processing (DSP).Piscataway,NJ:IEEE Press,2011:1-5.
[10]PEI Y,WU Y,JIA D.Image denoising based on bidimensional empirical mode decomposition[C]∥2011 International Conference on Mechatronic Science,Electric Engineering and Computer (MEC).Piscataway,NJ:IEEE Press,2011:1122-1125.
[11]BHUIYAN S M A,ADHAMI R R,KHAN J F.A novel approach of fast and adaptive bidimensional empirical mode decomposition∥2008 IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEE Press,2008:1313-1316.
[12]CHEN L,WANG K,MENG Q Y,et al.Application of Two-Dimensional Empirical Mode Decomposition Method in Magnetic Flux Leakage Image Processing[J].Laboratory Research and Exploration,2012,31(6):28-31.
[13]LAHMIRI S.Image denoising in bidimensional empirical mode decomposition domain:the role of Student’s probability distribution function[J].Healthcare technology letters,2015,3(1):67-71.
[14]AN F P,LIN D C,ZHOU X W,et al.Enhancing image denoising performance of bidimensional empirical mode decomposition by improving the edge effect[J].International Journal of Antennas and Propagation,2015,2015(12):1-12.
[15]LIU D,CHEN X.Image denoising based on improved bidimensional empirical mode decomposition thresholding technology[J].Multimedia Tools and Applications,2019,76(8):7381-7417.
[16]BHUIYAN S M A,ADHAMI R R,KHAN J F.Fast and Adaptive Bidimensional Empirical Mode Decomposition Using Order-Statistics Filter Based Envelope Estimation [J].Eurasip Journal on Advances in Signal Processing,2008,2008(164):1-18.
[17]BHUIYAN S M A,ADHAMI R R,KHAN J F.A novel approach of fast and adaptive bidimensional empirical mode decomposition[C]∥2008 IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEE Press,2008:1313-1316.
[18]BARNDORFF-NIELSEN O E.Normal inverse Gaussian distributions and stochastic volatility modelling[J].Scandinavian Journal of statistics,1997,24(1):1-13.
[19]FATHI A,NAGHSH-NILCHI A R.Efficient image denoisingmethod based on a new adaptive wavelet packet thresholding function[J].IEEE Transactions on Image Processing,2012,21(9):3981-3990.
[20]CHANG S G,YU B,VETTERLI M.Adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing,2000,9(9):1532-1546.
[21]LIU J,HUANG T Z,SELESNICK I W,et al.Image restoration using total variation with overlapping group sparsity[J].Information Sciences,2015,295(a):232-246.
[22]MICCHELLI C A,SHEN L,XU Y.Proximity algorithms forimage models:denoising[J].Inverse Problems,2011,27(4):045009.
[23]WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE transactions on image processing,2004,13(4):600-612.
[24]ZHANG X.A Method for Image Denoising Based on Normal Inverse Gaussian Model Using Bayesian Estimation [J].Acta Optica Sinica,2010,30(1):70-74.
[25]JIA J,CHEN L.Image Denoising of Nonsubsampled Contourlet Transform Based on Normal Inverse Gaussian Model[J].Chinese Journal of Electronics,2011,39(7):1563-1568.
[26]LAN X Y,CHEN L,JIA J,et al.Image denoising algorithmbased on improved normal inverse Gaussian distribution model[J].Journal of Computer Applications,2017,34(10):3188-3192.
[27]BHUIYAN M I H,AHMAD M O,SWAMY M N S.Wavelet-based despeckling of medical ultrasound images with the symmetric normal inverse Gaussian prior[C]∥2007 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP’07).Piscataway,NJ:IEEE Press,2007:I-721-I-724.
[1] DING Chang,YIN Qing-bo and LU Ming-yu. Summary of Partial Differential Equation (PDE) Method on Digital Image Processing [J]. Computer Science, 2013, 40(Z11): 341-346.
[2] . [J]. Computer Science, 2008, 35(10): 246-249.
[3] . [J]. Computer Science, 2007, 34(2): 256-257.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.