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http://dx.doi.org/10.13067/JKIECS.2020.15.1.161

An Extension of Unified Bayesian Tikhonov Regularization Method and Application to Image Restoration  

Yoo, Jae Hung (Dept. of Computer Engineering, Chonnam Nat. Univ.)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.15, no.1, 2020 , pp. 161-166 More about this Journal
Abstract
This paper suggests an extension of the unified Bayesian Tikhonov regularization method. The unified method establishes the relationship between Tikhonov regularization parameter and Bayesian hyper-parameters, and presents a formula for obtaining the regularization parameter using the maximum posterior probability and the evidence framework. When the dimension of the data matrix is m by n (m >= n), we derive that the total misfit has the range of m ± n instead of m. Thus the search range is extended from one to 2n + 1 integer points. Golden section search rather than linear one is applied to reduce the time. A new benchmark for optimizing relative error and new model selection criteria to target it are suggested. The experimental results show the effectiveness of the proposed method in the image restoration problem.
Keywords
Golden Section Search; Image Restoration; Model Selection Criteria; Unified Bayesian Tikhonov Regularization;
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Times Cited By KSCI : 5  (Citation Analysis)
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