基于字典算法的OCT图像散斑稀疏降噪
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摘要
光学相干层析扫描(OCT)作为一种新型无创高分辨率扫描方式,在临床上得到广泛应用,但是OCT图像本身存在严重的散斑噪声,这大大影响了疾病的诊断。本文针对OCT图像中的乘性散斑噪声,改进了两种原始字典降噪算法。该算法首先对OCT图像进行对数变换,采用正交匹配追踪算法进行稀疏编码,以及K奇异值分解学习算法进行自适应字典的更新,最后通过加权平均以及指数变换回到空域。实验结果表明,本文改进的两种字典算法能有效降低OCT图像中的散斑噪声,获得良好的视觉效果。并通过均方误差(MSE)、峰值信噪比(PSNR)、结构相似性(SSIM)以及边缘保持指数(EPI)四个指标评价降噪效果,与两种原始字典降噪算法和传统滤波算法相比,两种改进字典算法降噪效果优于其他算法,其中自适应字典算法表现更好。
As a new non-invasive and high-resolution scanning method, optical coherence tomography(OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error(MSE), peak signal-to-noise ratio(PSNR), structural similarity(SSIM) and edge-preserving index(EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.
As a new non-invasive and high-resolution scanning method, optical coherence tomography(OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error(MSE), peak signal-to-noise ratio(PSNR), structural similarity(SSIM) and edge-preserving index(EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.
引文
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[3]Xiang S H,Zhou L,Schmitt J M.Speckle noise reduction for optical coherence tomography[J].Proceedings of SPIE,1998,3196:79-88.
[4]Jung C R,Schacanski J.Adaptive image denoising in scale-space using the wavelet transform[C]//Proceedings of the14th Brazilian Symposium on Computer Graphics and Image Processing,Florianopolis,Brazil,2001:172-178.
[5]Schmitt J M.Array detection for speckle reduction in optical coherence microscopy[J].Physics in Medicine&Biology,1997,42(7):1427-1439.
[6]Pircher M,Gotzinger E,Leitgeb R,et al.Speckle reduction in optical coherence tomography by frequency compounding[J].Journal of Biomedical Optics,2003,8(3):565-569.
[7]Romano Y,Elad M.Improving K-SVD denoising by post-processing its method-noise[C]//Proceedings of 2013IEEE International Conference on Image Processing,Melbourne,VIC,Australia,2013:435-439.
[8]Tang X O,Wang X G.Face sketch synthesis and recognition[C]//Proceedings of the 9th IEEE International Conference on Computer Vision,Nice,France,2003:687-694.
[9]Elad M,Aharon M.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[10]Elad M.Sparse and Redundant Representations:From Theory to Applications in Signal and Image Processing[M].New York:Springer,2010:1094-1097.
[11]Bruckstein A M,Donoho D L,Elad M.From sparse solutions of systems of equations to sparse modeling of signals and images[J].SIAM Review,2009,51(1):34-81.
[12]Zhang Y S,Ji K F,Deng Z P,et al.Clustering-based SAR image denoising by sparse representation with KSVD[C]//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium,Beijing,2016:5003-5006.
[13]Song X R,Wu L D,Hao H X.Hyperspectral image denoising base on adaptive sparse representation[C]//Proceedings of2018 IEEE Third International Conference on Data Science in Cyberspace,Guangzhou,2018:735-739.
[14]Nguyen T T,Trinh D H,Linh-Trung N.An efficient example-based method for CT image denoising based on frequency decomposition and sparse representation[C]//Proceedings of2016 International Conference on Advanced Technologies for Communications,Hanoi,Vietnam,2016:293-296.
[15]Yang P,Gao L F,Wang J,et al.A color image denoising algorithm based on sparse representation and dictionary learning[J].Computer Engineering&Science,2018,40(5):842-848.杨培,高雷阜,王江,等.基于稀疏表示与字典学习的彩色图像去噪算法[J].计算机工程与科学,2018,40(5):842-848.
[16]Pyatykh S,Hesser J,Zheng L.Image noise level estimation by principal component analysis[J].IEEE Transactions on Image Processing,2013,22(2):687-699.
[17]He J T,Chen M H,Jia W Y,et al.Segmentation of diabetic macular edema in OCT retinal images[J].Opto-Electronic Engineering,2018,45(7):170605.何锦涛,陈明惠,贾文宇,等.眼底OCT图像中糖尿病性黄斑水肿的分割[J].光电工程,2018,45(7):170605.
[18]Huynh-Thu Q,Ghanbari M.Scope of validity of PSNR in image/video quality assessment[J].Electronics Letters,2008,44(13):800-801.
[19]Wang Z,Bovik A C,Sheikh H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEETransactions on Image Processing,2004,13(4):600-612.
[20]Deng J X,Liang Y M.Noise reduction with wavelet transform in optical coherence tomographic images[J].Acta Optica Sinica,2009,29(8):2138-2141.邓菊香,梁艳梅.光学相干层析图像的小波去噪方法研究[J].光学学报,2009,29(8):2138-2141.
[21]Donoho D L,Johnstone I M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.