# Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging

arXiv: https://arxiv.org/abs/2203.01695 · HTML: https://mlsanity.com/paper/2203.01695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds](https://mlsanity.com/paper/1808.10038/) (2018)
- [Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems](https://mlsanity.com/paper/1505.05114/) (2015)
- [Learned reconstructions for practical mask-based lensless imaging](https://mlsanity.com/paper/1908.11502/) (2019)
- [Phase Retrieval from Coded Diffraction Patterns](https://mlsanity.com/paper/1310.3240/) (2013)
- [On the Power of Preconditioning in Sparse Linear Regression](https://mlsanity.com/paper/2106.09207/) (2021)
- [Sparse Phase Retrieval via Truncated Amplitude Flow](https://mlsanity.com/paper/1611.07641/) (2016)
- [Unfolded Algorithms for Deep Phase Retrieval](https://mlsanity.com/paper/2012.11102/) (2020)
- [UPR: A Model-Driven Architecture for Deep Phase Retrieval](https://mlsanity.com/paper/2003.04396/) (2020)
- [Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations](https://mlsanity.com/paper/1605.07719/) (2016)
- [Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork](https://mlsanity.com/paper/2101.04348/) (2021)
- [Power-Balanced Hybrid Optics Boosted Design for Achromatic Extended-Depth-of-Field Imaging via Optimized Mixed OTF](https://mlsanity.com/paper/2103.05720/) (2021)
- [ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging](https://mlsanity.com/paper/2105.07891/) (2021)
- [Non-Convex Recovery from Phaseless Low-Resolution Blind Deconvolution Measurements using Noisy Masked Patterns](https://mlsanity.com/paper/2111.13670/) (2021)

## Abstract (arXiv)

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene resulting in coded diffraction patterns at the sensor. Recently, the hybrid approach of model-driven network or deep unfolding has emerged as an effective alternative to conventional model-based and learning-based phase retrieval techniques because it allows for bounding the complexity of algorithms while also retaining their efficacy. Additionally, such hybrid approaches have shown promise in improving the design of DOEs that follow theoretical uniqueness conditions. There are opportunities to exploit novel experimental setups and resolve even more complex DOE phase retrieval applications. This paper presents an overview of algorithms and applications of deep unfolding for bootstrapped - regardless of near, middle, and far zones - phase retrieval.