Researchers show amplitude‑based methods can recover low‑rank matrices from noisy magnitude data, proving error ≈ ‖η‖₂/√p when measurements p ≳ (m+n)r. Sep 2025. Read more: https://getnews.me/new-study-evaluates-amplitude-based-models-for-low-rank-matrix-recovery/ #phaseretrieval #lowrank
September 30, 2025 at 11:13 PM
Researchers show amplitude‑based methods can recover low‑rank matrices from noisy magnitude data, proving error ≈ ‖η‖₂/√p when measurements p ≳ (m+n)r. Sep 2025. Read more: https://getnews.me/new-study-evaluates-amplitude-based-models-for-low-rank-matrix-recovery/ #phaseretrieval #lowrank
Koki Yamada et al.: Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm ##Ptychography##PhaseRetrieval##DeepNeuralNetworks@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724006897
Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm
This paper proposes a ptychographic phase-retrieval algorithm combined with a deep neural network (DNN). The proposed method allows the measurement model to be explicitly incorporated into the DNN-based approach, improving imaging capability and robustness to changes in experimental conditions.
journals.iucr.org
August 20, 2024 at 12:01 AM
Koki Yamada et al.: Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm ##Ptychography##PhaseRetrieval##DeepNeuralNetworks@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724006897
Researchers show that in the Poisson regime, non-convex and convex least-squares estimators attain the minimax-optimal O(√n/m) error bound; the preprint was posted on 1 Oct 2025. https://getnews.me/stable-phase-retrieval-optimal-rates-in-poisson-heavy-tailed-noise/ #phaseretrieval #poissonnoise
October 3, 2025 at 5:21 AM
Researchers show that in the Poisson regime, non-convex and convex least-squares estimators attain the minimax-optimal O(√n/m) error bound; the preprint was posted on 1 Oct 2025. https://getnews.me/stable-phase-retrieval-optimal-rates-in-poisson-heavy-tailed-noise/ #phaseretrieval #poissonnoise
Researchers used an event‑based vision sensor with sub‑millisecond latency, showing event capture removed sequential exposures and improved low‑light phase retrieval. Read more: https://getnews.me/event-based-vision-sensors-enable-faster-low-light-phase-retrieval/ #eventbasedvision #phaseretrieval
October 6, 2025 at 9:28 AM
Researchers used an event‑based vision sensor with sub‑millisecond latency, showing event capture removed sequential exposures and improved low‑light phase retrieval. Read more: https://getnews.me/event-based-vision-sensors-enable-faster-low-light-phase-retrieval/ #eventbasedvision #phaseretrieval
Natsuki Akaishi et al.: Subgradient-projection-based stable phase-retrieval algorithm for X-ray ptychography ##HardXRayPtychography##PhaseRetrieval##SubgradientProjection@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724004709
Subgradient-projection-based stable phase-retrieval algorithm for X-ray ptychography
This paper proposes a phase-retrieval algorithm for X-ray ptychography with almost the same computational efficiency as the conventional methods. It exploits an optimization technique, subgradient projection, which has an interesting property that means it can be expected to avoid yielding poor images.
journals.iucr.org
July 5, 2024 at 12:00 AM
Natsuki Akaishi et al.: Subgradient-projection-based stable phase-retrieval algorithm for X-ray ptychography ##HardXRayPtychography##PhaseRetrieval##SubgradientProjection@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724004709
Jens Lucht et al.: HoToPy: a toolbox for X-ray holo-tomography in Python ##XRayImaging##PhaseRetrieval##ComputedTomography@unihh@uniGoettingen...##IUCrhttps://journals.iucr.org/paper?S1600577525008550
HoToPy: a toolbox for X-ray holo-tomography in Python
We present HoToPy, a toolbox for X-ray holo-tomography in Python. It offers state-of-the-art phase retrieval methods for synchrotron and laboratory sources as well as tomographic reconstruction and alignment methods.
journals.iucr.org
October 30, 2025 at 1:00 AM
Jens Lucht et al.: HoToPy: a toolbox for X-ray holo-tomography in Python ##XRayImaging##PhaseRetrieval##ComputedTomography@unihh@uniGoettingen...##IUCrhttps://journals.iucr.org/paper?S1600577525008550
gESP initialization expands the class of signals recoverable with Ω(k log n) sample complexity in sparse phase retrieval and needs fewer measurements than prior methods. Read more: https://getnews.me/new-algorithm-boosts-sample-efficiency-in-sparse-phase-retrieval/ #sparse #phaseretrieval
September 22, 2025 at 9:45 PM
gESP initialization expands the class of signals recoverable with Ω(k log n) sample complexity in sparse phase retrieval and needs fewer measurements than prior methods. Read more: https://getnews.me/new-algorithm-boosts-sample-efficiency-in-sparse-phase-retrieval/ #sparse #phaseretrieval
Optimal frames for phase retrieval map to polygons that maximize perimeter for a fixed diameter, and harmonic frames aren’t optimal for any even m ≥ 4. Read more: https://getnews.me/optimal-frames-for-phase-retrieval-linked-to-polygon-geometry/ #phaseretrieval #polygongeometry #harmonicframes
October 7, 2025 at 10:40 PM
Optimal frames for phase retrieval map to polygons that maximize perimeter for a fixed diameter, and harmonic frames aren’t optimal for any even m ≥ 4. Read more: https://getnews.me/optimal-frames-for-phase-retrieval-linked-to-polygon-geometry/ #phaseretrieval #polygongeometry #harmonicframes
An Editors' Pick via #OPG_OpEx: Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram https://bit.ly/4mTgzMw #XRayTomography #PhaseRetrieval @brookhavenlab.bsky.social
Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram
X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for...
opg.optica.org
September 8, 2025 at 2:20 PM
An Editors' Pick via #OPG_OpEx: Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram https://bit.ly/4mTgzMw #XRayTomography #PhaseRetrieval @brookhavenlab.bsky.social
A preconditioned subgradient method achieves linear convergence with ill‑conditioned smooth maps, with linear rate. It reaches solutions faster in phase retrieval. Read more: https://getnews.me/preconditioned-subgradient-method-boosts-composite-optimization-speed/ #preconditioned #phaseretrieval
October 6, 2025 at 5:30 PM
A preconditioned subgradient method achieves linear convergence with ill‑conditioned smooth maps, with linear rate. It reaches solutions faster in phase retrieval. Read more: https://getnews.me/preconditioned-subgradient-method-boosts-composite-optimization-speed/ #preconditioned #phaseretrieval