计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 21-27.doi: 10.11896/jsjkx.200800183

• 图像处理&多媒体技术 • 上一篇    下一篇

基于自适应加权重复值滤波和同态滤波的MR图像增强

黄雪冰, 魏佳艺, 沈文宇, 凌力   

  1. 复旦大学信息科学与工程学院 上海200433
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 凌力(lingli@fudan.edu.cn)
  • 作者简介:xbhuang19@fudan.edu.cn
  • 基金资助:
    国家重点研发计划(2018YFB2101100)

MR Image Enhancement Based on Adaptive Weighted Duplicate Filtering and Homomorphic Filtering

HUANG Xue-bing, WEI Jia-yi, SHEN Wen-yu, LING Li   

  1. School of Information Science and Technology,Fudan University,Shanghai 200433,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:HUANG Xue-bing,born in 1998,postgraduate.His main research interests include network communication and security,blockchain technology,big data analysis and cloud computing.
    LING Li,born in 1967,professor.His main research interests include network communication and security.
  • Supported by:
    National Key R&D Program of China (2018YFB2101100).

摘要: 磁共振(Magnetic Resonance,MR)图像通常存在椒盐噪声(Salt and Pepper Noise,SPN)以及对比度低的问题,为了增强MR图像,分别在空域和频域针对不同侧重点分步进行滤波。对于多数滤波算法去除高水平SPN不理想的情况,提出了自适应加权重复值滤波算法(Adaptive Weighted Duplicate Filter,AWDF),通过连续放大窗口直到两个连续窗口的最大值和最小值分别相等来确定自适应窗口大小,用窗口内最大重复无噪像素的均值替代噪声像素。将其应用于不同噪声水平下的MR图像的预处理中,再在频域应用同态滤波。仿真结果表明,用自适应加权重复值滤波器和优化的高斯同态滤波器相结合的办法处理MR图像,能够在去除高水平SPN的同时提高图像对比度,增加图像细节,对图像的PSNR和SSIM等都有较大提高,图像增强效果显著。

关键词: 磁共振图像, 椒盐噪声, 同态滤波, 图像增强, 自适应滤波

Abstract: Magnetic resonance (MR) images are usually affected by salt and pepper noise (SPN) and low contrast.In this paper,we enhance the MR images by filtering the images in the spatial and frequency domain respectively.Since most of the existing filtering algorithms are not ideal for removing high-level SPN,we propose adaptive weighted duplicate filter (AWDF).The adaptive window size is determined by continuously enlarging the window until the maximum and minimum values of the two successive windows are equal respectively,and then replacing the noise pixel with the mean value of the most duplicate noise-free pixels in the window.We apply the algorithm to the pre-processing of MR images with different SPN levels,and then apply homomorphic filtering in the frequency domain.The simulation results show that the method of combining AWDF and optimized Gaussian homomorphic filter can improve the contrast and details of the images while removing high-level SPN.The PSNR and SSIM of the image have been greatly improved,and the enhancement is remarkable.

Key words: Adaptive filtering, Homomorphic filtering, Image enhancement, Magnetic resonance images, Salt and pepper noise

中图分类号: 

  • TP391.41
[1] XIONG G X.Principle of magnetic resonance imaging[M].Science Press,2007.
[2] ALI H M.A new method to remove salt & pepper noise inMagnetic Resonance Images[C]//International Conference on Computer Engineering & Systems.IEEE,2016.
[3] MOHANTY F,RUP S,DASH B.A Thresholding-Based Saltnd Pepper Noise Removal Using B-Spline Interpolation in MRI Images[C]//International Conference on Computational Intelligence & Communication Networks.IEEE,2016.
[4] DEEPA B,SUMITHRA M G.Comparative analysis of noise removal techniques in MRI brain images[C]//2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).IEEE,2016.
[5] TAHIR M,ANAS I,ABDUL S.A Review Paper of Various Filters for Noise Removal in MRI Brain Image[J].International Journal of Innovative Research in Computer and Communication Engineering,2016,4(12):21711-21715.
[6] GOKILAVANI C,RAJESWARAN N,KARPAGAABIRAMI S,et al.Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise and Salt & Pepper Noise in Retinal Images[J].Middle-East Journal of Scientific Research,2016,24(2):475-478.
[7] YUGANDER P,TEJASWINI C H,MEENAKSHI J,et al.MRImage Enhancement using Adaptive Weighted Mean Filtering and Homomorphic Filtering[J].Procedia Computer Science,2020,167:677-685.
[8] HWANG H,HADDAD R A.Adaptive Median Filters:New Algorithms and Results[J].IEEE Transactions on Image Proces-sing,1995,4(4):499-502.
[9] TOH K K V,N-A-MAT I.Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction[J].IEEE Signal Processing Letters,2010,17(3):281-284.
[10] ZHANG P X,FANG L.A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise[J].IEEE Signal Processing Letters,2014,21(10):1280-1283.
[11] ERKAN U,LEVENT G.A new method based on pixel density in salt and pepper noise removal[J].Turkish Journal of Electrical Engineering & Computer Sciences,2018,26(1):162-171.
[12] GARG B,SATTI P,SHARMA N.Min-Max Average Poolingbased Filter for Impulse Noise Removal[J].Signal Processing Letters,IEEE,2020,PP(99).
[13] JMAL M,SOUIDENE W,ATTIA R.New color image illumination enhancement technique based on homomorphic filtering[C]//European Workshop on Visual Information Processing.IEEE,2015.
[14] TSENG C C,LEE S L.A weak-illumation image enhancement method uisng homomorphic filter and image fusion[C]//2017 IEEE 6th Global Conference on Consumer Electronics.IEEE,2017.
[15] HAN S,LIU W,XING W.Image Enhancement Based on SpatialMulti-scale Homomorphic Filtering and Local Entropy Guided Image Filtering[C]//2017 IEEE 15th Intl Conf on Dependable,Autonomic and Secure Computing,15th Intl Conf on Pervasive Intelligence and Computing,3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).IEEE,2018.
[16] KAUR K,JINDAL N,SINGH K.Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images[J].Multimedia Tools and Applications,2019,78(19):27891-27914.
[17] LIU X,YANG X,WANG D,et al.Detecting Pulse Rates from Facial Videos Recorded in Unstable Lighting Conditions:an Adaptive Spatio-Temporal Homomorphic Filtering Algorithm[J].IEEE Transactions on Instrumentation and Measurement,2020.DOI:10.1109/TIM.2020.3021222.
[18] PAN W Q,TU J J,GAN Z L,et al.Low Light Images Enhancement Based on Retinex Adaptive Reflectance Estimation and LIPS Post-processing[J].Computer Science,2019,46(8):327-331.
[19] KAUR K,NEERU J,KULBIR S.Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images[J].Multimedia Tools and Applications,2019,78(19):27891-27914.
[20] GONZALEZ R C,WOODS R E.Digital image processing[J].Prentice Hall International,2008,28(4):484-486.
[21] SI H,HU F Y,GU Y J,et al.Improved Denoising AlgorithmBased on Non-regular Area Gaussian Filtering[J].Computer Science,2014,41(11):313-316.
[22] XIAO X Y,JING W B.An Improved Image Enhancement AlgorithmBased on the Peak-Signal to Noise Ratio[J].Journal of Changchun University of Science and Technology(Natural Science Edition),2017,40(4):83-86.
[23] PRAYLIN SELVA BLESSY S A,HELEN SULOCHANA C.Enhanced Homomorphic Unsharp Masking method for intensity inhomogeneity correction in brain MR images[J].Computer Methods in Biomechanics and Biomedical Engineering:Imaging &Visualization,2020,8(1):40-48.
[1] 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮.
基于DNGAN的磁共振图像超分辨率重建算法
Super-resolution Reconstruction of MRI Based on DNGAN
计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105
[2] 赵明华, 周童童, 都双丽, 石争浩.
基于虚拟曝光方法的单幅逆光图像增强
Single Backlit Image Enhancement Based on Virtual Exposure Method
计算机科学, 2022, 49(6A): 384-389. https://doi.org/10.11896/jsjkx.210400243
[3] 赵征鹏, 李俊钢, 普园媛.
基于卷积神经网络的Retinex低照度图像增强
Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network
计算机科学, 2022, 49(6): 199-209. https://doi.org/10.11896/jsjkx.210400092
[4] 杨秀璋, 武帅, 夏换, 于小民.
基于自适应图像增强技术的水族文字提取与识别研究
Research on Shui Characters Extraction and Recognition Based on Adaptive Image Enhancement Technology
计算机科学, 2021, 48(6A): 74-79. https://doi.org/10.11896/jsjkx.200900070
[5] 李昌兴, 雷柳, 张晓璐.
基于形态学图像增强和PCNN的脑部CT与MRI图像融合
Brain CT and MRI Image Fusion Based on Morphological Image Enhancement and PCNN
计算机科学, 2020, 47(10): 194-199. https://doi.org/10.11896/jsjkx.190700185
[6] 徐敏敏, 寇光杰, 马云艳, 岳峻, 贾世祥, 张志旺.
基于PCNN内部活动项的彩色图像增强算法
Color Image Enhancement Algorithm Based on PCNN Internal Activities
计算机科学, 2019, 46(6A): 259-262.
[7] 周丽军, 刘晓.
基于分数阶傅里叶变换的隧道低对比度裂缝检测
Low-contrast Crack Detection Method Based on Fractional Fourier Transform
计算机科学, 2019, 46(6A): 208-210.
[8] 姚哲维, 杨丰, 黄靖, 刘娅琴.
改进型循环生成对抗网络的血管内超声图像增强
Improved CycleGANs for Intravascular Ultrasound Image Enhancement
计算机科学, 2019, 46(5): 221-227. https://doi.org/10.11896/j.issn.1002-137X.2019.05.034
[9] 汪晓妍, 刘琪琪, 黄晓洁, 姜娓娓, 夏明.
基于空间对齐和轮廓匹配的颈动脉多对比MRI三维配准方法
Multi-contrast Carotid MRI 3D Registration Method Based on Spatial Alignment and Contour Matching
计算机科学, 2019, 46(5): 241-246. https://doi.org/10.11896/j.issn.1002-137X.2019.05.037
[10] 杨秀璋, 夏换, 于小民.
一种基于水族濒危文字的图像增强及识别方法
Image Enhancement and Recognition Method Based on Shui-characters
计算机科学, 2019, 46(11A): 324-328.
[11] 刘洋, 张杰, 张慧.
一种改进的Retinex算法在图像去雾中的研究与应用
Study and Application of Improved Retinex Algorithm in Image Defogging
计算机科学, 2018, 45(6A): 242-243.
[12] 周丽军.
基于图像增强与分水岭分割的隧道低对比度裂缝提取方法
Low-contrast Crack Extraction Method Based on Image Enhancement and Watershed Segmentation
计算机科学, 2018, 45(6A): 259-261.
[13] 赵军辉,吴玉峰,胡坤融,蒲斌.
基于Lab色彩空间和色调映射的彩色图像增强算法
Color Image Enhancement Algorithm Based on Lab Color Space and Tone Mapping
计算机科学, 2018, 45(2): 297-300. https://doi.org/10.11896/j.issn.1002-137X.2018.02.051
[14] 张翔, 王伟, 肖迪.
一种改进的具有色彩保护的多尺度Retinex图像增强算法
Improved Image Enhancemengt Algorithm Based on Multi-scale Retinex with Chromaticity Preservation
计算机科学, 2018, 45(10): 246-249. https://doi.org/10.11896/j.issn.1002-137X.2018.10.045
[15] 马洪晋, 聂玉峰.
基于二级修复的多方向加权均值滤波算法
Multi-directional Weighted Mean Denoising Algorithm Based on Two Stage Noise Restoration
计算机科学, 2018, 45(10): 250-254. https://doi.org/10.11896/j.issn.1002-137X.2018.10.046
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!