sDR

sDR improves convergence and robustness of ptychographic reconstruction by applying a semi-implicit relaxed Douglas-Rachford algorithm to recover images from diffraction patterns, particularly under sparse-data conditions.

Key Features:

• Improved Convergence: sDR accelerates convergence in ptychographic reconstruction relative to ePIE and rPIE.
• Enhanced Reconstruction Quality: sDR yields superior reconstruction quality for ptychographic imaging.
• Robustness with Sparse Data: sDR maintains convergence and reconstruction performance when diffraction-pattern sampling is sparse, where ePIE and rPIE may fail or slow.
• General Applicability: sDR is applicable across physical and biological science contexts that employ ptychographic reconstruction.

Scientific Applications:

• Ptychographic imaging of physical and biological samples: sDR supports accurate reconstruction of samples from collected diffraction patterns in experimental imaging studies.
• Sparse-data experiments: sDR enables reconstruction in situations where acquiring extensive diffraction patterns is impractical.

Methodology:

Iterative accelerated algorithm that incorporates a semi-implicit relaxation into the Douglas-Rachford framework and builds on classical ptychographic approaches such as ePIE and rPIE to address convergence issues with sparse diffraction-data.