# Computationally Efficient Information-Driven Optical Design with Interchanging Optimization

arXiv: https://arxiv.org/abs/2507.07789 · HTML: https://mlsanity.com/paper/2507.07789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging](https://mlsanity.com/paper/2008.02683/) (2020)
- [Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop](https://mlsanity.com/paper/1912.05098/) (2019)
- [Task-Driven Lens Design](https://mlsanity.com/paper/2305.17185/) (2023)
- [Fourier DiffuserScope: Single-shot 3D Fourier light field microscopy with a diffuser](https://mlsanity.com/paper/2006.16343/) (2020)
- [Information-driven design of imaging systems](https://mlsanity.com/paper/2405.20559/) (2024)
- [Designing lensless imaging systems to maximize information capture](https://mlsanity.com/paper/2506.08513/) (2025)

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

- [End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout](https://mlsanity.com/paper/2606.09792/) (2026)

## Abstract (arXiv)

Recent work has demonstrated that imaging systems can be evaluated through the information content of their measurements alone, enabling application-agnostic optical design that avoids computational decoding challenges. Information-Driven Encoder Analysis Learning (IDEAL) was proposed to automate this process through gradient-based optimization. In this work, we study IDEAL across diverse imaging systems and find that it suffers from high memory usage, long runtimes, and a potentially mismatched objective function due to end-to-end differentiability requirements. We introduce IDEAL with Interchanging Optimization (IDEAL-IO), a method that decouples density estimation from optical parameter optimization by alternating between fitting models to current measurements and updating optical parameters using fixed models for information estimation. This approach reduces runtime and memory usage by up to 6x while enabling more expressive density models that guide optimization toward superior designs. We validate our method on diffractive optics, lensless imaging, and snapshot 3D microscopy applications, establishing information-theoretic optimization as a practical, scalable strategy for real-world imaging system design.