# Variational Depth from Focus Reconstruction

arXiv: https://arxiv.org/abs/1408.0173 · HTML: https://mlsanity.com/paper/1408.0173/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2014.

## Built on by (most cited first, 15 of 15)

- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Deep Depth from Focus with Differential Focus Volume](https://mlsanity.com/paper/2112.01712/) (2021)
- [Fully Self-Supervised Depth Estimation from Defocus Clue](https://mlsanity.com/paper/2303.10752/) (2023)
- [HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors](https://mlsanity.com/paper/2407.18443/) (2024)
- [Learning Depth from Focus in the Wild](https://mlsanity.com/paper/2207.09658/) (2022)
- [Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance](https://mlsanity.com/paper/2202.13055/) (2022)
- [Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus](https://mlsanity.com/paper/2409.12323/) (2024)
- [Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus](https://mlsanity.com/paper/2201.06823/) (2022)
- [Dynamic Fusion Network For Light Field Depth Estimation](https://mlsanity.com/paper/2104.05969/) (2021)
- [Learning Multi-modal Information for Robust Light Field Depth Estimation](https://mlsanity.com/paper/2104.05971/) (2021)
- [Learning Monocular Depth from Focus with Event Focal Stack](https://mlsanity.com/paper/2405.06944/) (2024)
- [Robust Shape from Focus via Multiscale Directional Dilated Laplacian and Recurrent Network](https://mlsanity.com/paper/2512.10498/) (2025)
- [Depth from Defocus via Direct Optimization](https://mlsanity.com/paper/2602.18509/) (2026)
- [Towards Minimal Focal Stack in Shape from Focus](https://mlsanity.com/paper/2604.01603/) (2026)
- [Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis](https://mlsanity.com/paper/2607.13601/) (2026)

## Abstract (arXiv)

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.