# Optimal Sample Lens Positioning in Digital Camera Systems

arXiv: https://arxiv.org/abs/2407.01789 · HTML: https://mlsanity.com/paper/2407.01789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Learning to Autofocus](https://mlsanity.com/paper/2004.12260/) (2020)
- [Towards Real-World Focus Stacking with Deep Learning](https://mlsanity.com/paper/2311.17846/) (2023)
- [Intelligent Autofocus](https://mlsanity.com/paper/2002.12389/) (2020)

## Abstract (arXiv)

In contemporary imaging systems, achieving optimal auto-focus (AF) performance hinges on precise lens positioning. Extensive research has delved into refining algorithms for determining the ideal lens position across passive, active, and hybrid autofocus systems. This paper explores the mathematical intricacies and practical considerations essential for optimizing lens positions during focus searches, addressing overarching challenges encountered in AF systems, such as balancing speed and accuracy. Moreover, the lens position calculations proposed herein can be applied to various focus algorithms, including focus bracketing. The proposed method offers adaptability and scalability, rendering it suitable for integration into a wide array of camera systems, ranging from smartphones and DSLRs to microscopes and industrial imaging devices.