Variational Depth From Focus Reconstruction

Moeller, Michael; Benning, Martin; Schönlieb, Carola-Bibiane; Cremers, Daniel

doi:10.1109/tip.2015.2479469

Cited by 100 publications

(87 citation statements)

References 34 publications

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“…However, the proposed method produces better performance and is more robust. The average RMSE of WF, variational DFF (VDFF) [14] and SG-DFF are presented in Table 2. The estimated depth maps are shown in Figure 7.…”

Section: Resultsmentioning

confidence: 99%

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Semi-global depth from focus

Liu

Key

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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Reconstructing depth map from a sequence of images with different camera settings, known as Depth from Focus (DFF), has been addressed for decades. However, Classical local methods, e.g., windowed filter, usually produce uncertain estimation in textureless areas and depth discontinuities. Meanwhile, they appear sensitive to filter size and image noise. In this research a semi-global depth from focus approach that considers both the accuracy and robustness is proposed to enforce an adaptive smoothness constraint based on modified Laplacian measure. Firstly an adaptive aggregation strategy is proposed to integrate the local focus measure and then an efficient optimization method, known as scanline optimization, is introduced to approximate the global optimal of depth estimation. To evaluate the effectiveness of this research, experiments on both synthetic and real datasets are performed. The experimental results show that the proposed approach generates more robust and accurate depth maps while preserving depth discontinuities.

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Section: Resultsmentioning

confidence: 99%

“…Local nonlinear methods for enhancing the focus volume have been proposed in [5] [4]. Global optimization techniques were also employed to DFF, such as Markov Random Field(MRF) [7] and variational method [14]. These methods enforce the smooth constraints in two dimensions.…”

Section: Related Workmentioning

confidence: 99%

Semi-global depth from focus

Liu

Key

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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“…It is important to emphasize that even for nonsmooth, nonconvex optimization there is a vast amount of recent publications, ranging from forward-backward, respectively proximaltype, schemes [8,9,10,49,50], over linearized proximal schemes [365,47,366,298], to inertial methods [299,309], primal-dual algorithms [361,267,279,34], scaled gradient projection methods [310], nonsmooth Gauß-Newton extensions [149,300] and nonlinear Eigenproblems [206,59,32,51,261,31]. We focus mainly on recent generalizations of the proximal gradient method and the linearized Bregman iteration for nonconvex functionals E in the following.…”

Section: Nonconvex Optimizationmentioning

confidence: 99%

Modern regularization methods for inverse problems

2018

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Regularization methods are a key tool in the solution of inverse problems. They are used to introduce prior knowledge and make the approximation of ill-posed (pseudo-)inverses feasible. In the last two decades interest has shifted from linear towards nonlinear regularization methods even for linear inverse problems. The aim of this paper is to provide a reasonably comprehensive overview of this development towards modern nonlinear regularization methods, including their analysis, applications, and issues for future research.In particular we will discuss variational methods and techniques derived from those, since they have attracted particular interest in the last years and link to other fields like image processing and compressed sensing. We further point to developments related to statistical inverse problems, multiscale decompositions, and learning theory.

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“…The complexity of the non-convex optimization is reformulated in [6] where depth from focus is presented as a variational problem by introducing a nonconvex data fidelity term and a convex nonsmooth regularization. The nonconvex minimization problem in [6] is aimed to be solved by a linearized alternating directions method of multipliers. This method has a superior performance in comparison to state of the art methods but the convergence of the optimization function happens very slowly and in a high number of iterations.…”

Section: Previous Workmentioning

confidence: 99%

Application of preconditioned alternating direction method of multipliers in depth from focal stack

Javidnia

Corcoran

2018

J. Electron. Imag.

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Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.

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Variational Depth From Focus Reconstruction

Cited by 100 publications

References 34 publications

Semi-global depth from focus

Semi-global depth from focus

Modern regularization methods for inverse problems

Application of preconditioned alternating direction method of multipliers in depth from focal stack

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