This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus or shape from focus. We propose to state the depth from focus 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. Additionally, we propose to solve the nonconvex minimization problem with a linearized alternating directions method of multipliers (ADMM), allowing to minimize the energy very efficiently. A numerical comparison to classical methods on simulated as well as on real data is presented.
@article{arxiv.1408.0173,
title = {Variational Depth from Focus Reconstruction},
author = {Michael Moeller and Martin Benning and Carola Schönlieb and Daniel Cremers},
journal= {arXiv preprint arXiv:1408.0173},
year = {2015}
}