全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于噪声水平估计的图像盲去噪*

DOI: 10.16451/j.cnki.issn1003-6059.201501007, PP. 50-58

Keywords: 图像去噪,三维块匹配(BM3D),噪声水平估计,图像盲去噪

Full-Text   Cite this paper   Add to My Lib

Abstract:

三维块匹配(BM3D)去噪是当前去噪性能最好的算法之一.但由于时间复杂度较高,而且需要输入精确的图像噪声水平参数,极大地限制该算法的广泛应用.因此,文中首先采用基于网格的块匹配策略,提出快速三维块匹配(FBM3D)算法.然后提出基于迭代的盲图像噪声水平估计算法,由SVM学习算法确定迭代的初始值,再由图像质量判定迭代是否终止.测试实验表明,与原始的BM3D算法相比,该算法在计算效率、视觉感知效果和定量评测方面均有明显改善.

References

[1]  Takeda H, Farsiu S, Milanfar P. Kernel Regression for Image Processing and Reconstruction. IEEE Trans on Image Processing, 2007, 16(2): 349-366
[2]  Buades A, Coll B, Morel J M. A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 2005, 4(2): 490-530
[3]  Dabov K, Foi A, Katkovnik V, et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Trans on Image Processing, 2007, 16(8): 2080-2095
[4]  Chatterjee P, Milanfar P. Patch-Based Near-Optimal Image Denoising. IEEE Trans on Image Processing, 2012, 21(4): 1635-1649
[5]  Dabov K, Foi A, Katkovnik V, et al. BM3D Image Denoising with Shape-Adaptive Principal Component Analysis // Proc of the Workshop on Signal Processing with Adaptive Sparse Structured Representations. Saint Malo, France, 2009
[6]  Levin A, Nadler B. Natural Image Denoising: Optimality and Inherent Bounds // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 2833-2840
[7]  Danielyan A, Katkovnik V, Egiazarian K. BM3D Frames and Variational Image Deblurring. IEEE Trans on Image Processing, 2012, 21(4): 1715-1728
[8]  Maggioni M, Katkovnik V, Egiazarian K, et al. Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction. IEEE Trans on Image Processing, 2013, 22(1): 119-133
[9]  Lebrun M. An Analysis and Implementation of the BM3D Image Denoising Method. Image Processing Online, 2012, 2: 175-213
[10]  Abramov S K, Lukin V V, Vozel B, et al. Segmentation-Based Method for Blind Evaluation of Noise Variance in Images. Journal of Applied Remote Sensing, 2008. DOI:10.1117/1.2977788
[11]  Uss M, Vozel B, Lukin V, et al. Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters. EURASIP Journal on Advances in Signal Processing, 2011. DOI:10.1155/2011/806516
[12]  Pyatykh S, Hesser J, Zheng L. Image Noise Level Estimation by Principal Component Analysis. IEEE Trans on Image Processing, 2013, 22(2): 687-699
[13]  Zhu X, Milanfar P. Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content. IEEE Trans on Image Processing, 2010, 19(12): 3116-3132
[14]  Mittal A, Soundararajan R, Bovik A C. Making a "Completely Blind" Image Quality Analyzer. IEEE Signal Processing Letters, 2013, 20(3): 209-212
[15]  Mittal A, Moorthy A K, Bovik A C. No-Reference Image Quality Assessment in the Spatial Domain. IEEE Trans on Image Processing, 2012, 21(12): 4695-4708
[16]  Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, 2011. DOI:10.1145/1961189.1961199
[17]  Tarel J P, Hautiere N. Fast Visibility Restoration from a Single Color or Gray Level Image // Proc of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan, 2009: 2201-2208
[18]  Jin L H, Li D H. An Image Denoising Algorithm Based on Noise Detection. Pattern Recognition and Artificial Intelligence, 2008, 21(3): 298-302 (in Chinese) (金良海,李德华.基于噪声检测的图像去噪算法.模式识别与人工智能, 2008, 21(3): 298-302)
[19]  Niu H M, Du Q, Zhang J X. An Algorithm of Adaptive Total Variation Image Denoising. Pattern Recognition and Artificial Intelligence, 2011, 24(6): 798-803 (in Chinese) (牛和明,杜 茜,张建勋.一种自适应全变分图像去噪算法.模式识别与人工智能, 2011, 24(6): 798-803)
[20]  Tomasi C, Manduchi R. Bilateral Filtering for Gray and Color Images // Proc of the 6th IEEE International Conference on Computer Vision. Bombay, India, 1998: 839-846

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133