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FrDo-Net: a deep unfolding network for phase retrieval based on an adaptive fractional-domain physical layer
Optics Express
|August 14, 2026
Summary
We introduce a novel fractional-domain deep unfolding network (FrDo-Net) for phase retrieval. FrDo-Net adaptively optimizes the energy-focusing domain, significantly improving reconstruction accuracy and noise robustness, especially in low signal-to-noise conditions.
Area of Science:
- Optics and Photonics
- Computational Imaging
- Signal Processing
Background:
- Deep unfolding networks for phase retrieval often use fixed bases, leading to basis mismatch and energy dispersion with non-stationary light fields.
- Existing methods struggle with processing complex optical fields and are susceptible to noise, limiting their practical application.
Purpose of the Study:
- To develop an adaptive deep unfolding network for robust phase retrieval.
- To overcome the limitations of fixed bases and improve performance under challenging conditions like low signal-to-noise ratios.
Main Methods:
- Proposed a fractional-domain deep unfolding network (FrDo-Net) with learnable fractional Fourier transform (FrFT) orders.
- Integrated a Swin transformer to capture long-range diffraction dependencies.
- Developed a differentiable pre-computed Eigen-decomposition-based FrFT (PCE-FrFT) operator for efficient end-to-end optimization.
Main Results:
- FrDo-Net adaptively identifies the optimal energy-focusing domain for phase retrieval.
- The network effectively decouples signals from strong shot noise, even at extremely low signal-to-noise ratios.
- Demonstrated superior reconstruction accuracy, texture fidelity, and noise robustness compared to state-of-the-art methods.
Conclusions:
- FrDo-Net offers a significant advancement in phase retrieval by adaptively optimizing the transform domain.
- The proposed method provides robust and accurate phase retrieval performance under severe noise conditions.
- This work paves the way for more reliable computational imaging systems dealing with non-stationary optical fields.
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