Improved BM3D image denoising using SSIM-optimized Wiener filter

Image denoising is considered a salient pre-processing step in sophisticated imaging applications. Over the decades, numerous studies have been conducted in denoising. Recently proposed Block matching and 3D (BM3D) filtering added a new dimension to the study of denoising. BM3D is the current state-of-the-art of denoising and is capable of achieving better denoising as compared to any other existing method. However, there is room to improve BM3D to achieve high-quality denoising. In this study, to improve BM3D, we first attempted to improve the Wiener filter (the core of BM3D) by maximizing the structural similarity (SSIM) between the true and the estimated image, instead of minimizing the mean square error (MSE) between them. Moreover, for the DC-only BM3D profile, we introduced a 3D zigzag thresholding. Experimental results demonstrate that regardless of the type of the image, our proposed method achieves better denoising performance than that of BM3D.

[1]  Yongdong Zhang,et al.  Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification , 2018, IEEE Transactions on Intelligent Transportation Systems.

[2]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[3]  Stefan Harmeling,et al.  Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Mahmoud R. El-Sakka,et al.  Structural Similarity Optimized Wiener Filter: A Way to Fight Image Noise , 2015, ICIAR.

[5]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[8]  Matthias Nussbaum,et al.  Advanced Digital Signal Processing And Noise Reduction , 2016 .

[9]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[10]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[11]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Maria Petrou,et al.  Image processing - the fundamentals , 1999 .

[13]  Fang Qiu,et al.  Speckle Noise Reduction in SAR Imagery Using a Local Adaptive Median Filter , 2004 .

[14]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[15]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[16]  Kostadin Dabov,et al.  BM3D Image Denoising with Shape-Adaptive Principal Component Analysis , 2009 .

[17]  Robert W. Heath,et al.  Design of Linear Equalizers Optimized for the Structural Similarity Index , 2008, IEEE Transactions on Image Processing.

[18]  Hans Volkmer,et al.  A characterization of the normal distribution , 2014, J. Stat. Theory Appl..

[19]  Karen O. Egiazarian,et al.  Video denoising by sparse 3D transform-domain collaborative filtering , 2007, 2007 15th European Signal Processing Conference.

[20]  Abdul Rehman,et al.  SSIM-based non-local means image denoising , 2011, 2011 18th IEEE International Conference on Image Processing.

[21]  Karen O. Egiazarian,et al.  Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space , 2007, 2007 IEEE International Conference on Image Processing.

[22]  Peyman Milanfar,et al.  Is Denoising Dead? , 2010, IEEE Transactions on Image Processing.

[23]  Karen O. Egiazarian,et al.  Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction , 2013, IEEE Transactions on Image Processing.

[24]  Robert W. Heath,et al.  A Linear Estimator Optimized for the Structural Similarity Index and its Application to Image Denoising , 2006, 2006 International Conference on Image Processing.

[25]  Mahmoud R. El-Sakka,et al.  SRAD with Weighted Diffusion Function , 2013, ICIAR.

[26]  Peyman Milanfar,et al.  Patch-Based Near-Optimal Image Denoising , 2012, IEEE Transactions on Image Processing.

[27]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[28]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[29]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[30]  Valero Laparra,et al.  Divisive normalization image quality metric revisited. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Mohammad Mahedi Hasan.  Adaptive Edge-guided Block-matching and 3D filtering (BM3D) Image Denoising Algorithm , 2014 .

[32]  Yongdong Zhang,et al.  Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles , 2018, IEEE Transactions on Intelligent Transportation Systems.

[33]  Bahadir K. Gunturk,et al.  Image Restoration , 2012 .

[34]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[35]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[36]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[37]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[38]  Marc Lebrun,et al.  An Analysis and Implementation of the BM3D Image Denoising Method , 2012, Image Process. Line.

[39]  Jessica Koehler,et al.  Advanced Digital Signal Processing And Noise Reduction , 2016 .

[40]  You Sai Zhang,et al.  BM3D Denoising Algorithm with Adaptive Block-Match Thresholds , 2012 .

[41]  M. Rothenberg.  A new inverse-filtering technique for deriving the glottal air flow waveform during voicing. , 1970, The Journal of the Acoustical Society of America.

[42]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.