# Fovea Stacking: Imaging with Dynamic Localized Aberration Correction

arXiv: https://arxiv.org/abs/2506.00716 · HTML: https://mlsanity.com/paper/2506.00716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 6 of 6)

- [Curriculum Learning for ab initio Deep Learned Refractive Optics](https://mlsanity.com/paper/2302.01089/) (2023)
- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Light Stage Super-Resolution: Continuous High-Frequency Relighting](https://mlsanity.com/paper/2010.08888/) (2020)
- [Fully Self-Supervised Depth Estimation from Defocus Clue](https://mlsanity.com/paper/2303.10752/) (2023)
- [Mutual-Guided Dynamic Network for Image Fusion](https://mlsanity.com/paper/2308.12538/) (2023)
- [End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model](https://mlsanity.com/paper/2406.00834/) (2024)

## Built on by (most cited first, 1 of 1)

- [Guidestar-Free Adaptive Optics with Asymmetric Apertures](https://mlsanity.com/paper/2602.07029/) (2026)

## Abstract (arXiv)

The desire for cameras with smaller form factors has recently lead to a push for exploring computational imaging systems with reduced optical complexity such as a smaller number of lens elements. Unfortunately such simplified optical systems usually suffer from severe aberrations, especially in off-axis regions, which can be difficult to correct purely in software. In this paper we introduce Fovea Stacking , a new type of imaging system that utilizes emerging dynamic optical components called deformable phase plates (DPPs) for localized aberration correction anywhere on the image sensor. By optimizing DPP deformations through a differentiable optical model, off-axis aberrations are corrected locally, producing a foveated image with enhanced sharpness at the fixation point - analogous to the eye's fovea. Stacking multiple such foveated images, each with a different fixation point, yields a composite image free from aberrations. To efficiently cover the entire field of view, we propose joint optimization of DPP deformations under imaging budget constraints. Due to the DPP device's non-linear behavior, we introduce a neural network-based control model for improved alignment between simulation-hardware performance. We further demonstrated that for extended depth-of-field imaging, fovea stacking outperforms traditional focus stacking in image quality. By integrating object detection or eye-tracking, the system can dynamically adjust the lens to track the object of interest-enabling real-time foveated video suitable for downstream applications such as surveillance or foveated virtual reality displays