Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering

In order to improve speckle noise denoising of block matching 3d filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs a NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3D denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5dB. The MLFE-BM3D method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.

[1]  Md Kamrul Hasan,et al.  Blind Deconvolution of Ultrasound Images Using $l_{1}$ -Norm-Constrained Block-Based Damped Variable Step-Size Multichannel LMS Algorithm , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[2]  Edmund Y Lam,et al.  Wavelet domain compounding for speckle reduction in optical coherence tomography , 2013, Journal of biomedical optics.

[3]  Alexei A. Efros,et al.  Light field video capture using a learning-based hybrid imaging system , 2017, ACM Trans. Graph..

[4]  Peyman Milanfar,et al.  Fundamental limits of image denoising: Are we there yet? , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Richard G. Baraniuk,et al.  ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems , 2004, IEEE Transactions on Signal Processing.

[6]  J. Goodman.  Some fundamental properties of speckle , 1976 .

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

[8]  Shutian Liu,et al.  Computational coherent imaging by rotating a cylindrical lens. , 2018, Optics express.

[9]  Nian Cai,et al.  Image denoising method based on a deep convolution neural network , 2017, IET Image Process..

[10]  Jiangwei Zhang,et al.  Improved BM3D denoising method , 2017, IET Image Process..

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

[12]  Zhengjun Liu,et al.  Two noise-robust axial scanning multi-image phase retrieval algorithms based on Pauta criterion and smoothness constraint. , 2017, Optics express.

[13]  Khalid M. Hosny,et al.  Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators , 2018 .

[14]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[15]  Deguang Han,et al.  Super-Wavelets and Decomposable Wavelet Frames , 2005 .

[16]  Michael Unser,et al.  Learning Convex Regularizers for Optimal Bayesian Denoising , 2017, IEEE Transactions on Signal Processing.

[17]  Jaakko Astola,et al.  From Local Kernel to Nonlocal Multiple-Model Image Denoising , 2009, International Journal of Computer Vision.

[18]  Nelson D. A. Mascarenhas,et al.  Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D , 2017, IEEE Transactions on Image Processing.

[19]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[20]  M. Srinivasan,et al.  Robust deconvolution of high-frequency ultrasound images using higher-order spectral analysis and wavelets , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[21]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[22]  Ali Molaei,et al.  Experimental Results of a 3-D Millimeter-Wave Compressive Reflector Antenna Imaging System , 2018, IEEE Antennas and Wireless Propagation Letters.

[23]  Liu Jie,et al.  Shift-invariant Non-aliasing Multiscale Geometric Analysis Filter Banks: Theory, Design and Application , 2014, ICIMCS '14.

[24]  Ghazanfar Latif,et al.  Speckle suppression in medical ultrasound images through Schur decomposition , 2018, IET Image Process..

[25]  Jin Liu,et al.  3D Feature Constrained Reconstruction for Low-Dose CT Imaging , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[26]  Igor Djurovic.  BM3D filter in salt-and-pepper noise removal , 2016, EURASIP J. Image Video Process..

[27]  A. Oraevsky,et al.  Laser optoacoustic imaging system for detection of breast cancer. , 2009, Journal of biomedical optics.

[28]  Minh N. Do,et al.  Nonsubsampled contourlet transform: construction and application in enhancement , 2005, IEEE International Conference on Image Processing 2005.

[29]  Quan Zhang,et al.  Noise reduction for low-dose X-ray CT based on fuzzy logical in stationary wavelet domain , 2013 .

[30]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[31]  Corina Nafornita,et al.  Image Denoising Using a New Implementation of the Hyperanalytic Wavelet Transform , 2009, IEEE Transactions on Instrumentation and Measurement.

[32]  Qi Li,et al.  Range image noise suppression in laser imaging system , 2009 .

[33]  Minh N. Do,et al.  Contourlets: a directional multiresolution image representation , 2002, Proceedings. International Conference on Image Processing.

[34]  Jian Sun,et al.  BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering , 2018, IEEE Signal Processing Letters.

[35]  Peyman Milanfar,et al.  Practical Bounds on Image Denoising: From Estimation to Information , 2011, IEEE Transactions on Image Processing.

[36]  Wen-Liang Hwang,et al.  Wavelet Bayesian Network Image Denoising , 2013, IEEE Transactions on Image Processing.

[37]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

[38]  Yong Cheng,et al.  Comments on "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering" , 2011, IEEE Trans. Image Process..

[39]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Amine Bermak,et al.  A Block-Matching and 3-D Filtering Algorithm for Gaussian Noise in DoFP Polarization Images , 2018, IEEE Sensors Journal.

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

[42]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[43]  A. A. Bini,et al.  Noise classification and automatic restoration system using non-local regularization frameworks , 2018, The Imaging Science Journal.

[44]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[45]  Jiawei He,et al.  PML Algorithm for Positron Emission Tomography Combined With Nonlocal Fuzzy Anisotropic Diffusion Filtering , 2012, IEEE Transactions on Nuclear Science.

[46]  Jing-Yu Yang,et al.  IBM3D: Integer BM3D for Efficient Image Denoising , 2018, Circuits Syst. Signal Process..

[47]  A. Tannenbaum,et al.  Despeckling of medical ultrasound images , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[48]  Myung-Ho In,et al.  Reduced acoustic noise in diffusion tensor imaging on a compact MRI system , 2018, Magnetic resonance in medicine.

[49]  Jean-Michel Morel,et al.  A Nonlocal Bayesian Image Denoising Algorithm , 2013, SIAM J. Imaging Sci..

[50]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[51]  Alin Achim,et al.  Novel Bayesian multiscale method for speckle removal in medical ultrasound images , 2001, IEEE Transactions on Medical Imaging.