# Data Driven Coded Aperture Design for Depth Recovery

arXiv: https://arxiv.org/abs/1705.10021 · HTML: https://mlsanity.com/paper/1705.10021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2017.

## Built on by (most cited first, 4 of 4)

- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging](https://mlsanity.com/paper/2402.18102/) (2024)
- [Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture](https://mlsanity.com/paper/2309.08033/) (2023)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)

## Abstract (arXiv)

Inserting a patterned occluder at the aperture of a camera lens has been shown to improve the recovery of depth map and all-focus image compared to a fully open aperture. However, design of the aperture pattern plays a very critical role. Previous approaches for designing aperture codes make simple assumptions on image distributions to obtain metrics for evaluating aperture codes. However, real images may not follow those assumptions and hence the designed code may not be optimal for them. To address this drawback we propose a data driven approach for learning the optimal aperture pattern to recover depth map from a single coded image. We propose a two stage architecture where, in the first stage we simulate coded aperture images from a training dataset of all-focus images and depth maps and in the second stage we recover the depth map using a deep neural network. We demonstrate that our learned aperture code performs better than previously designed codes even on code design metrics proposed by previous approaches.