Blind Image Denoising Based on Noise Level Estimation
FANG Shuai1, XIA Xiu-Shan1, CAO Yang2, YU Lei1
1.School of Computer and Information, Hefei University of Technology, Hefei 230009 2.Department of Automation, University of Science and Technology of China, Hefei 230027
Abstract：Block-matching and 3D filtering (BM3D) algorithm is one of the best image denoising algorithms. However, the application of the algorithm is constrained owing to high time complexity and the requirement of exact image noise level parameter. Thus, a fast block-matching and 3D filtering (FBM3D) algorithm is proposed, which uses a grid-based block-matching strategy. Then, the image noise is refined by iteration in which the starting point is set by SVM learning and the ending point is decided by image quality. The experimental results show that the proposed algorithm has a significant improvement in computation efficiency, visual effects and quantifiable performance evaluation.
[1] 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) [2] 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) [3] 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 [4] Takeda H, Farsiu S, Milanfar P. Kernel Regression for Image Processing and Reconstruction. IEEE Trans on Image Processing, 2007, 16(2): 349-366 [5] 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 [6] 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 [7] Chatterjee P, Milanfar P. Patch-Based Near-Optimal Image Denoising. IEEE Trans on Image Processing, 2012, 21(4): 1635-1649 [8] 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 [9] 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 [10] Danielyan A, Katkovnik V, Egiazarian K. BM3D Frames and Variational Image Deblurring. IEEE Trans on Image Processing, 2012, 21(4): 1715-1728 [11] 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 [12] Lebrun M. An Analysis and Implementation of the BM3D Image Denoising Method. Image Processing Online, 2012, 2: 175-213 [13] 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 [14] 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 [15] Pyatykh S, Hesser J, Zheng L. Image Noise Level Estimation by Principal Component Analysis. IEEE Trans on Image Processing, 2013, 22(2): 687-699 [16] 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 [17] Mittal A, Soundararajan R, Bovik A C. Making a "Completely Blind" Image Quality Analyzer. IEEE Signal Processing Letters, 2013, 20(3): 209-212 [18] 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 [19] 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 [20] 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
2015, Vol. 28 
