An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR).

[1]  S. Aiguo,et al.  Evolving Gaussian RBF network for nonlinear time series modelling and prediction , 1998 .

[2]  Wang-Q Lim,et al.  Optimally Sparse Image Representations using Shearlets , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[3]  Min Xia,et al.  Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs , 2012, Knowl. Based Syst..

[4]  Vishal M. Patel,et al.  Directional Multiscale Processing of Images Using Wavelets with Composite Dilations , 2012, Journal of Mathematical Imaging and Vision.

[5]  Wang-Q Lim,et al.  Sparse multidimensional representation using shearlets , 2005, SPIE Optics + Photonics.

[6]  Quan-sen Sun,et al.  An Image Filter Based on Multiobjective Genetic Algorithm and Shearlet Transformation , 2013 .

[7]  Hamid Krim,et al.  Edge detection and processing using shearlets , 2008, 2008 15th IEEE International Conference on Image Processing.

[8]  Glenn R. Easley,et al.  Shearlet-Based Total Variation Diffusion for Denoising , 2009, IEEE Transactions on Image Processing.

[9]  Hamid Krim,et al.  A Shearlet Approach to Edge Analysis and Detection , 2009, IEEE Transactions on Image Processing.

[10]  Minh N. Do,et al.  The finite ridgelet transform for image representation , 2003, IEEE Trans. Image Process..

[11]  Chokri Ben Amar,et al.  Sparse Multi-regularized Shearlet-Network Using Convex Relaxation for Face Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.

[12]  Wavelets Associated with Composite Dilations Demetrio , 2005 .

[13]  Quansen Sun,et al.  An Improved Image Denoising Algorithm based on Shearlet , 2013 .

[14]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[15]  Manuel A. Duarte-Mermoud,et al.  Probabilistic Adaptive Crossover Applied to Chilean Wine Classification , 2013 .

[16]  Hanqing Lu,et al.  Improving kernel Fisher discriminant analysis for face recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Aiguo Song,et al.  An immune evolutionary algorithm for sphericity error evaluation , 2004 .

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

[19]  Hanqing Lu,et al.  Head tracking using shapes and adaptive color histograms , 2002, Journal of Computer Science and Technology.

[20]  Chen Xi.  Particle Swarm Optimization Based Adaptive Image Denoising in Shearlet Domain , 2011 .

[21]  刘青山,et al.  Kernel-Based Nonlinear Discriminant Analysis for Face Recognition , 2003 .

[22]  Wai Keung Wong,et al.  A seasonal discrete grey forecasting model for fashion retailing , 2014, Knowl. Based Syst..

[23]  Zhiyong Fan,et al.  Fault detection and estimation for non-Gaussian stochastic systems with time varying delay , 2013 .

[24]  Stéphane Mallat,et al.  A review of Bandlet methods for geometrical image representation , 2007, Numerical Algorithms.

[25]  Zhijie Wang,et al.  Temporal association based on dynamic depression synapses and chaotic neurons , 2011, Neurocomputing.

[26]  Aiguo Song,et al.  An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network , 2013 .

[27]  Aiguo Song,et al.  Active Perception-Based Haptic Texture Sensor , 2013 .

[28]  Aiguo Song,et al.  Delay-range-dependent Stability Criteria of Neural Networks with Time-varying Discrete and Distributed Delays , 2014 .

[29]  Xi Chen,et al.  Shearlet-Based Adaptive Shrinkage Threshold for Image Denoising , 2010, 2010 International Conference on E-Business and E-Government.

[30]  Lehrstuhl für Elektrische,et al.  Gaussian swarm: a novel particle swarm optimization algorithm , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[31]  Min Xia,et al.  Sequence Memory Based on Ordered Pattern Interrelation , 2012 .