Variational Depth From Focus Reconstruction

被引：83

作者：

Moeller, Michael ^{[1
]}

Benning, Martin ^{[2
]}

Schoenlieb, Carola ^{[2
]}

Cremers, Daniel ^{[1
]}

机构：

[1] Tech Univ Munich, Dept Comp Sci, D-85748 Munich, Germany

[2] Ctr Math Sci, Dept Appl Math & Theoret Phys, Cambridge CB3 0WA, England

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2015年 / 24卷 / 12期

基金：

英国工程与自然科学研究理事会; 欧洲研究理事会;

关键词：

Depth from focus; depth estimation; nonlinear variational methods; alternating directions method of multipliers; SHAPE; ALGORITHM; MINIMIZATION; CONVERGENCE;

D O I：

10.1109/TIP.2015.2479469

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus (DFF) or shape from focus. We propose to state the DFF problem as a variational problem, including a smooth but nonconvex data fidelity term and a convex nonsmooth regularization, which makes the method robust to noise and leads to more realistic depth maps. In addition, we propose to solve the nonconvex minimization problem with a linearized alternating directions method of multipliers, allowing to minimize the energy very efficiently. A numerical comparison to classical methods on simulated as well as on real data is presented.

引用

页码：5369 / 5378

页数：10

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