Optimizing illumination for precise multi-parameter estimations in coherent diffractive imaging

Abstract

Coherent diffractive imaging (CDI) is widely used to characterize structured samples from measurements of diffracting intensity patterns. We introduce a numerical framework to quantify the precision that can be achieved when estimating any given set of parameters characterizing the sample from measured data. The approach, based on the calculation of the Fisher information matrix, provides a clear benchmark to assess the performance of CDI methods. Moreover, by optimizing the Fisher information metric using deep learning optimization libraries, we demonstrate how to identify the optimal illumination scheme that minimizes the estimation error under specified experimental constrains. This work paves the way for an efficient characterization of structured samples at the sub-wavelength scale.

References in corpus (6)

• Multi-parameter Quantum Metrology
• A Geometric Perspective on Quantum Parameter Estimation
• Sparsity-based single-shot sub-wavelength coherent diffractive imaging
• Content adaptive sparse illumination for Fourier ptychography
• Influence of the local scattering environment on the localization precision of single particles
• The CRLB and Maximum likelihood in ptychography with Poisson noise

Cited by in corpus (8)

• Deep learning in nano-photonics: inverse design and beyond
• Roadmap on Wavefront Shaping and deep imaging in complex media
• Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics
• Optimal control of coherent light scattering for binary decision problems
• Noise-robust latent vector reconstruction in ptychography using deep generative models
• Temporal shaping of wave fields for optimally precise measurements in scattering environments
• Model-free estimation of the Cramér-Rao bound for deep-learning microscopy in complex media
• Sensor fusion in ptychography