Ptychography
A computational imaging technique that reconstructs sample images from overlapping diffraction measurements. It exploits the redundant information provided by overlapping regions to achieve high-fidelity phase recovery and is widely used in X-ray imaging and other fields.
Timeline (last 90 days)
Ptychography is a computational imaging technique that reconstructs a sample image through overlapping diffraction measurements.
Ptychographic reconstruction is essentially a phase retrieval problem: the detector records the intensity of the diffraction pattern, the phase information is lost, and inversion must be performed computationally.
The role of scan overlap is to let adjacent illumination regions share information, forming an overdetermined system of equations and thereby improving the stability of the inversion.
Reducing scan overlap can significantly improve measurement efficiency, but the physical constraints are correspondingly weakened and the reconstruction problem becomes more ill-posed.
Traditional iterative methods often struggle to recover a clear sample structure under low-overlap conditions.
The diffusion model's prior is trained on natural images and has a domain gap with the complex-valued sample distribution of ptychography.
The diffusion prior can effectively reduce the measurement redundancy required for ptychographic reconstruction, providing a new avenue for improving imaging efficiency.
All Facts (7)
Ptychography is a computational imaging technique that reconstructs a sample image through overlapping diffraction measurements.
50%UnverifiedPtychographic reconstruction is essentially a phase retrieval problem: the detector records the intensity of the diffraction pattern, the phase information is lost, and inversion must be performed computationally.
50%UnverifiedThe role of scan overlap is to let adjacent illumination regions share information, forming an overdetermined system of equations and thereby improving the stability of the inversion.
50%UnverifiedReducing scan overlap can significantly improve measurement efficiency, but the physical constraints are correspondingly weakened and the reconstruction problem becomes more ill-posed.
50%UnverifiedTraditional iterative methods often struggle to recover a clear sample structure under low-overlap conditions.
50%UnverifiedThe diffusion model's prior is trained on natural images and has a domain gap with the complex-valued sample distribution of ptychography.
50%UnverifiedThe diffusion prior can effectively reduce the measurement redundancy required for ptychographic reconstruction, providing a new avenue for improving imaging efficiency.
50%