# Analysis and Optimization of Aperture Design in Computational Imaging

arXiv: https://arxiv.org/abs/1712.04541 · HTML: https://mlsanity.com/paper/1712.04541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

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

- [Can Shadows Reveal Biometric Information?](https://mlsanity.com/paper/2209.10077/) (2022)
- [Coded Illumination for Improved Lensless Imaging](https://mlsanity.com/paper/2111.12862/) (2021)
- [End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout](https://mlsanity.com/paper/2606.09792/) (2026)

## Abstract (arXiv)

There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems---and the associated optimum aperture coding---under simple but meaningful propagation and sensor models. Among other results, we show that when thermal noise dominates, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates, randomly generated masks with lower transmissivity offer greater performance. We also provide comparisons to classical pinhole cameras.