# Towards Real-World Focus Stacking with Deep Learning

arXiv: https://arxiv.org/abs/2311.17846 · HTML: https://mlsanity.com/paper/2311.17846/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Synthetic Depth-of-Field with a Single-Camera Mobile Phone](https://mlsanity.com/paper/1806.04171/) (2018)
- [Wide Activation for Efficient and Accurate Image Super-Resolution](https://mlsanity.com/paper/1808.08718/) (2018)
- [Deep Depth From Focus](https://mlsanity.com/paper/1704.01085/) (2017)
- [SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion](https://mlsanity.com/paper/1908.01703/) (2019)
- [MFFW: A new dataset for multi-focus image fusion](https://mlsanity.com/paper/2002.04780/) (2020)
- [An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion](https://mlsanity.com/paper/1910.13136/) (2019)
- [Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network](https://mlsanity.com/paper/1904.00198/) (2019)

## Built on by (most cited first, 3 of 3)

- [After the Party: Navigating the Mapping From Color to Ambient Lighting](https://mlsanity.com/paper/2508.02168/) (2025)
- [Optimal Sample Lens Positioning in Digital Camera Systems](https://mlsanity.com/paper/2407.01789/) (2024)
- [Generative Multi-Focus Image Fusion](https://mlsanity.com/paper/2512.21495/) (2025)

## Abstract (arXiv)

Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shallow depth of field and different focus planes. Existing deep learning approaches to the underlying multi-focus image fusion problem have limited applicability to real-world imagery since they are designed for very short image sequences (two to four images), and are typically trained on small, low-resolution datasets either acquired by light-field cameras or generated synthetically. We introduce a new dataset consisting of 94 high-resolution bursts of raw images with focus bracketing, with pseudo ground truth computed from the data using state-of-the-art commercial software. This dataset is used to train the first deep learning algorithm for focus stacking capable of handling bursts of sufficient length for real-world applications. Qualitative experiments demonstrate that it is on par with existing commercial solutions in the long-burst, realistic regime while being significantly more tolerant to noise. The code and dataset are available at https://github.com/araujoalexandre/FocusStackingDataset.