Image denoising based on modified discrete lattice Boltzmann method

Ning-ning YU

doi:10.3788/YJYXS20173204.0294

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Image denoising based on modified discrete lattice Boltzmann method

Display and Imag | 更新时间：2020-07-07

- Image denoising based on modified discrete lattice Boltzmann method
- Chinese Journal of Liquid Crystals and Displays Vol. 32, Issue 4, Pages: 294-301(2017)
- 作者机构：
  
  浙江越秀外国语学院网络传播学院, 浙江绍兴 312000
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20173204.0294
  CLC： TP391
- Received：20 October 2016，
  
  Accepted：07 December 2016，
  
  Published：2017-04
- 稿件说明：
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Ning-ning YU. Image denoising based on modified discrete lattice Boltzmann method[J]. Chinese journal of liquid crystals and displays, 2017, 32(4): 294-301.
DOI：

Ning-ning YU. Image denoising based on modified discrete lattice Boltzmann method[J]. Chinese journal of liquid crystals and displays, 2017, 32(4): 294-301. DOI： 10.3788/YJYXS20173204.0294.

摘要

为解决生物医学图像处理过程中存在的图像反差低下且噪声污染严重等问题，本文对离散格子玻尔兹曼方法进行了改进，提出了一种改进离散格子玻尔兹曼方法的图像去噪算法。算法对分段线性拉伸函数进行构造，并作为LBM公式的外力因子，构建演化求解方程，通过调节变化因子

，同步控制图像降噪与反差增强的相关程度，消除降噪与反差增强之间的对立；通过优化平衡分布加权系数，折中扩散方程的求解精度和速度；将边缘特征和迭代步长嵌入松弛因子中，实现医学图像非线性扩散的去噪，解决医学图像处理中的同步降噪与反差增强间的矛盾。仿真实验表明，该算法与其他降噪算法相比，在有效抑制噪声的同时，细节处理更为有效。

Abstract

In order to solve the problem of low image contrast and serious noise pollution in the process of biomedical image processing

a modified lattice Boltzmann method is proposed. The algorithm structures the piecewise linear stretching function

and constructed it as the external force factor of the LBM formula to build the evolution equation. The algorithm adjusts the change factor

to synchronously control the correlation between image noise reduction and contrast enhancement to eliminate the contrast between noise reduction and contrast enhancement. By optimizing the weighting coefficients of the equilibrium distribution

the solution accuracy and speed of the tradeoff diffusion equation are obtained. The edge feature and the iterative step are embedded in the relaxation factor to realize the non-linear diffusion denoising of the medical image and to solve the contradiction between the synchronous noise reduction and the contrast enhancement in the medical image processing. The simulation results show that the proposed algorithm is more effective than other noise reduction algorithms in suppressing noise effectively.

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references

韩红伟,苗加庆.基于WFCM算法在MRI图像分割中的应用[J].现代电子技术,2015,38(6):90-93.

HAN H W, MIAO J Q. Application of an improved WFCM-based algorithm in MRI image segmentation[J]. Modern Electronics Technique , 2015, 38(6):90-93. (in Chinese)

温军玲,严壮志,蒋皆恢.一种区域统计信息的格子波尔兹曼图像分割模型[J].应用科学学报,2016,34(1):49-57.

WEN J L, YAN Z Z, JIANG J H. A lattice Boltzmann model with statistic region information for image segmentation[J]. Journal of Applied Sciences, 2016, 34(1):49-57. (in Chinese)

温军玲,严壮志,林笑曼.格子波尔兹曼方法的医学图像同步去噪增强算法[J].小型微型计算机系统,2013,34(9):2197-2200.

WEN J L, YAN Z Z, LIN X M. Synchronization algorithm for denoising and enhancement of medical image based on lattice Boltzmann method[J]. Journal of Chinese Computer Systems, 2013, 34(9):2197-2200. (in Chinese)

宋茜,严壮志,周志崇,等.基于BI-RADS的超声乳腺图像的计算机辅助诊断研究[J].生物医学工程学进展,2009,30(1):9-13.

SONG Q, YAN Z Z, ZHOU Z C, et al. Computer-aided diagnosis of breast ultrasound image based on BI-RADS[J]. Progress in Biomedical Engineering, 2009, 30(1):9-13. (in Chinese)

陈玉,严壮志,钱跃竑.基于格子波尔兹曼模型的图像去噪[J].电子学报,2009,37(3):574-580.

CHEN Y, YAN Z Z, QIAN Y H. The lattice Boltzmann method based image denoising[J]. Acta Electronica Sinica , 2009, 37(3):574-580. (in Chinese)

滕刚,陈玉,吴倩.基于元胞自动机各向异性扩散模型的图像分割算法[J].计算机工程与设计,2010,31(16):3666-3669.

TENG G, CHEN Y, WU Q. Image segmentation based on lattice Boltzmann anisotropic diffusion model[J]. Computer Engineering and Design, 2010, 31(16):3666-3669. (in Chinese)

温军玲,严壮志,孙玉彪,等.集成边缘和区域信息的格子波尔兹曼模型图像分割[J].江苏大学学报(自然科学版),2013,34(6):687-692.

WEN J L, YAN Z Z, SUN Y B, et al. Image segmentation of integrated edge and region information by lattice Boltzmann model[J]. Journal of Jiangsu University (Natural Science Edition), 2013, 34(6):687-692. (in Chinese)

吴桑,彭良玉.基于小波变换的图像增强新算法[J].现代电子技术,2013,36(4):89-91.

WU S, PENG L Y. New image enhencement algorithm based on wavelet transform[J]. Modern Electronics Technique, 2013, 36(4):89-91. (in Chinese)

王大凯,侯榆青,彭进业.图像处理中的偏微分方程方法[M].北京:科学出版社,2008.

WANG D K, HOU Y Q, PENG J Y. Partial Differential Equation Methods on Image Processing[M]. Beijing:Science Press, 2008.

陈颖,彭进业,王大凯,等.基于PDE的图像去噪和反差增强同步算法[J].计算机工程,2009,35(23):224-226.

CHEN Y, PENG J Y, WANG D K, et al. Synchronization algorithm of image denoising and contrast enhancement based on PDE[J]. Computer Engineering , 2009, 35(23):224-226. (in Chinese)

韩希珍,陈朝东,赵健.基于PDE的非线性图像去噪与增强[J].液晶与显示,2011,26(1):111-115.

HAN X Z, CHEN C D, ZHAO J. Nonlinear image de-noising and enhancement based on PDE[J]. Chinese Journal of Liquid Crystals and Displays , 2011, 26(1):111-115. (in Chinese)

刘作楚,陈翰林,鲜大权.基于格子波尔兹曼的图像修复模型研究[J].西南科技大学学报,2013,28(2):64-68.

LIU Z C, CHEN H L, XIAN D Q. Study of lattice Boltzmann method based image inpainting[J]. Journal of Southwest University of Science and Technology , 2013, 28(2):64-68. (in Chinese)

CHANG Q S, YANG T. A lattice Boltzmann method for image denoising[J]. IEEE Transactions on Image Processing , 2009, 18(12):2797-2802.

LYSAKER M, LUNDERVOLD A, TAI X C. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time[J]. IEEE Transactions on Image Processing , 2003, 12(12):1579-1590.

KE J, HOU Y Q, WANG D K, et al. An improved algorithm for shape preserving contrast enhancement[J]. Acta Photonica Sinica , 2009, 38(1):214-219.

YANG M, ZHANG L, FENG X C, et al. Fisher discrimination dictionary learning for sparse representation[C]// Proceedings of IEEE International Conference on Computer Vision (ICCV), Barcelona:IEEE, 2011:543-550.

贾迪,董娜,孟祥福,等.基于矢量扩散控制的图像同步去噪增强方法[J].计算机工程,2014,40(6):230-233.

JIA D, DONG N, MENG X F, et al. Synchronization method of image denoising and enhancement based on vector diffusion control[J]. Computer Engineering, 2014, 40(6):230-233. (in Chinese)

肖贵仁,谢芳森,胡海江.改进型Q-NLM算法在医学图像去噪中的应用[J].计算机工程与应用,2015,51(10):174-176, 181.

XIAO G R, XIE F S, HU H J. Advanced Q-NLM algorithm application in medical image denoising[J]. Computer Engineering and Applications , 2015, 51(10):174-176, 181. (in Chinese)

LIN X B, QIU T S. Denoise MRI images using sparse 3D transformation domain collaborative filtering[C]// Proceedings of the 20114th International Conference on Biomedical Engineering and Informatics (BMEI) , Shanghai:IEEE, 2011:233-236.

刘巧红,李斌,林敏.结合复方向滤波器组高斯尺度混合模型及非局部均值滤波的图像去噪[J].光学精密工程,2014,22(10):2806-2814.

LIU Q H, LI B, LIN M. Image denoising with dual-directional filter bank GSM model and non-local mean filter[J]. Optics and Precision Engineering , 2014, 22(10):2804-2814. (in Chinese)

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