arXiv:2605.01122v2 Announce Type: replace Abstract: Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resu
The increasing computational demands of advanced imaging techniques, particularly in scientific research and industrial applications, are driving the need for more efficient reconstruction algorithms, making machine learning an attractive solution.
This development indicates a growing trend of integrating machine learning into complex scientific and engineering processes to overcome computational bottlenecks and significantly accelerate research and development cycles.
Traditional iterative ptychographic reconstruction, known for its slow convergence, can now be augmented and accelerated through learned fast-forward operators, potentially enabling faster experimental feedback and more complex imaging scenarios.
- · AI/ML researchers
- · Materials science
- · Semiconductor industry
- · Microscopy equipment manufacturers
- · Traditional algorithm developers (if they do not adapt)
- · Processes relying solely on conventional ptychography
Faster and more accurate analysis of nanoscale structures becomes possible.
New materials discovery and characterization will accelerate, impacting fields like drug development and advanced manufacturing.
The enhanced imaging capabilities could lead to breakthroughs in novel device fabrication and quality control that were previously restricted by computational time.
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Read at arXiv cs.LG