Automated Parameter Selection for Total Variation Minimization in Image Restoration

详细信息    查看全文

• 作者：Andreas Langer
• 关键词：Locally dependent regularization parameter ; Automated parameter selection ; Discrepancy principle ; Constrained/unconstrained problem ; Gaussian noise ; Impulse noise
• 刊名：Journal of Mathematical Imaging and Vision
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：57
• 期：2
• 页码：239-268
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Image Processing and Computer Vision; Applications of Mathematics; Signal,Image and Speech Processing; Mathematical Methods in Physics;
• 出版者：Springer US
• ISSN：1573-7683
• 卷排序：57

文摘

Algorithms for automatically selecting a scalar or locally varying regularization parameter for total variation models with an \(L^{\tau }\)-data fidelity term, \(\tau \in \{1,2\}\), are presented. The automated selection of the regularization parameter is based on the discrepancy principle, whereby in each iteration a total variation model has to be minimized. In the case of a locally varying parameter, this amounts to solve a multiscale total variation minimization problem. For solving the constituted multiscale total variation model, convergent first- and second-order methods are introduced and analyzed. Numerical experiments for image denoising and image deblurring show the efficiency, the competitiveness, and the performance of the proposed fully automated scalar and locally varying parameter selection algorithms.