Department of Mathematics
Blind deconvolution using generalized cross-validation approach to regularization parameter estimation
In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior. © 2011 IEEE.
Alternating minimization, blind deconvolution, generalized cross validation (GCV), regularization parameters, total variation (TV)
Source Publication Title
IEEE Transactions on Image Processing
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Liao, Haiyong, and Michael K. Ng. "Blind deconvolution using generalized cross-validation approach to regularization parameter estimation." IEEE Transactions on Image Processing 20.3 (2011): 670-680.