JOURNAL ARTICLE

Adaptive Box-Constrained Total Variation Image Restoration Using Iterative Regularization Parameter Adjustment Method

Zhining ZhuGuangcheng CaiYou‐Wei Wen

Year: 2015 Journal:   International Journal of Pattern Recognition and Artificial Intelligence Vol: 29 (07)Pages: 1554003-1554003   Publisher: World Scientific

Abstract

In this paper, we consider the problem of image restoration with box-constraints. Image restoration problem is ill-conditioned and the regularization approach has widely been used to stabilize the solution. The restored image highly depends on the choice of the regularization parameter. The regularization parameter is generally determined by trial-and-error method when no true original image is available. Obviously, it is time consuming. The main aim in this paper is to develop an algorithm to choose the regularization parameter automatically when the box-constraints are imposed. In the proposed algorithm, the regularization parameter is adaptively determined by the previous iterative solution. Numerical simulations are used to demonstrate the performance of the proposed method.

Keywords:
Regularization (linguistics) Image restoration Algorithm Mathematical optimization Image (mathematics) Computer science Mathematics Regularization perspectives on support vector machines Iterative method Total variation denoising Inverse problem Image processing Artificial intelligence Tikhonov regularization

Metrics

7
Cited By
2.02
FWCI (Field Weighted Citation Impact)
33
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Numerical methods in inverse problems
Physical Sciences →  Mathematics →  Mathematical Physics
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Sparse and Compressive Sensing Techniques
Physical Sciences →  Engineering →  Computational Mechanics
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