Choice of the Parameter for BM3D Denoising Algorithm Using No- Reference Metric

An automatic multiscale algorithm for Block-matching and 3D filtering (BM3D) method de noising parameter selection has been proposed. To optimize the filtering parameter the presence of retained structures in the ridge areas is analysed for the difference of the initial noisy and filtered images. Appearance of regular components on method noise is controlled using mutual information. An estimation of image characteristic details is based on Hessian matrix eigenvalues analysis. Images with added controlled Gaussian noise from general image TID2013 and BSDS500 databases were used for testing. It was found that the proposed no-reference metric outperforms existing no-reference metrics in selecting optimal denoising parameter. Algorithm calculation time does not depend on the image noise level and looks promising to be used in image adaptive BM3D-based methods.

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

[2]  Robert M. Haralick,et al.  Ridges and valleys on digital images , 1983, Comput. Vis. Graph. Image Process..

[3]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[4]  Xiang Zhu,et al.  Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content , 2010, IEEE Transactions on Image Processing.

[5]  Andrey S. Krylov,et al.  Image Ridge Denoising Using No-Reference Metric , 2017, ACIVS.

[6]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[9]  Karen O. Egiazarian,et al.  Weighted MSE based spatially adaptive BM3D , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[10]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[11]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[12]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[13]  Michael A. Malcolm,et al.  Computer methods for mathematical computations , 1977 .

[14]  Alexey Lukin,et al.  Methods of noise filtering quality assessment for CT image , 2013, DSP 2013.

[15]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[16]  Nikolay N. Ponomarenko,et al.  BM3D-HVS: Content-adaptive denoising for improved visual quality , 2017, Image Processing: Algorithms and Systems.

[17]  David H. Eberly,et al.  Ridges in Image and Data Analysis , 1996, Computational Imaging and Vision.

[18]  Lei Zheng,et al.  Image Noise Level Estimation by Principal Component Analysis , 2013, IEEE Transactions on Image Processing.

[19]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[20]  Antoine Manzanera Local Jet Based Similarity for NL-Means Filtering , 2010, 2010 20th International Conference on Pattern Recognition.

[21]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

[22]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[25]  Wotao Yin,et al.  An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..

[26]  Nikolay N. Ponomarenko,et al.  Color image database TID2013: Peculiarities and preliminary results , 2013, European Workshop on Visual Information Processing (EUVIP).