Blind Image Deconvolution Through Support Vector Regression

This letter introduces a new algorithm for the restoration of a noisy blurred image based on the support vector regression (SVR). Experiments show that the performance of the SVR is very robust in blind image deconvolution where the types of blurs, point spread function (PSF) support, and noise level are all unknown

[1]  Michael Margaliot,et al.  Nicholson's blowflies revisited: a fuzzy modeling approach , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[2]  Deepa Kundur,et al.  Blind image deconvolution revisited , 1996 .

[3]  Yehoshua Y. Zeevi,et al.  Quasi Maximum Likelihood Blind Deconvolution of Images Using Optimal Sparse Representations , 2003 .

[4]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[5]  David Salomon,et al.  Data Compression , 2000, Springer Berlin Heidelberg.

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[8]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[9]  Mostafa Kaveh,et al.  A regularization approach to joint blur identification and image restoration , 1996, IEEE Trans. Image Process..

[10]  Simone G. O. Fiori,et al.  Fast fixed-point neural blind-deconvolution algorithm , 2004, IEEE Transactions on Neural Networks.

[11]  J. C. Dainty,et al.  Iterative blind deconvolution method and its applications , 1988 .

[12]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[13]  Rudy Setiono,et al.  Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting , 1997, Neural Computation.

[14]  Li Chen,et al.  A soft double regularization approach to parametric blind image deconvolution , 2005, IEEE Transactions on Image Processing.

[15]  Jude W. Shavlik,et al.  Extracting Refined Rules from Knowledge-Based Neural Networks , 1993, Machine Learning.

[16]  Reginald L. Lagendijk,et al.  Regularized iterative image restoration with ringing reduction , 1988, IEEE Trans. Acoust. Speech Signal Process..

[17]  Kai-Yuan Cai,et al.  Extended neuro-fuzzy models of multilayer perceptrons , 2004, Fuzzy Sets Syst..

[18]  Michael Margaliot,et al.  Mathematical modeling of observed natural behavior: a fuzzy logic approach , 2004, Fuzzy Sets Syst..

[19]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .