High-throughput computational imaging requires efficient processing algorithms to retrieve multi-dimensional and multi-scale information. In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements. The existing PR algorithms suffer from the tradeoff among low computational complexity, robustness to measurement noise and strong generalization on different modalities. In this work, we report an efficient large-scale phase retrieval technique termed as LPR. It extends the plug-and-play generalized-alternating-projection framework from real space to nonlinear complex space. The alternating projection solver and enhancing neural network are respectively derived to tackle the measurement formation and statistical prior regularization. This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization. We applied the technique for a series of computational phase imaging modalities including coherent diffraction imaging, coded diffraction pattern imaging, and Fourier ptychographic microscopy. Extensive simulations and experiments validate that the technique outperforms the existing PR algorithms with as much as 17dB enhancement on signal-to-noise ratio, and more than one order-of-magnitude increased running efficiency. Besides, we for the first time demonstrate ultra-large-scale phase retrieval at the 8K level ($$7680\times 4320$$ 7680 × 4320 pixels) in minute-level time.
In order to increase signal-to-noise ratio in optical imaging, most detectors sacrifice resolution to increase pixel size in a confined area, which impedes further development of high throughput holographic imaging. Although the pixel super-resolution technique (PSR) enables resolution enhancement, it suffers from the trade-off between reconstruction quality and super-resolution ratio. In this work, we report a high-fidelity PSR phase retrieval method with plug-and-play optimization, termed PNP-PSR. It decomposes PSR reconstruction into independent sub-problems based on generalized alternating projection framework. An alternating projection operator and an enhancing neural network are employed to tackle the measurement fidelity and statistical prior regularization, respectively. PNP-PSR incorporates the advantages of individual operators, achieving both high efficiency and noise robustness. Extensive experiments show that PNP-PSR outperforms the existing techniques in both resolution enhancement and noise suppression.
A multi-scale model simulating hysteretic thermomechanical behavior of polycrystalline shape memory alloys (SMA) is presented. Using a kinetic Monte-Carlo approach, the energybased model estimates the stochastic average in terms of volume fraction for a phase or phase variant deriving from the Gibbs free energy density as a selection inside a given population. Indeed pseudo-elastic behavior for example is well-known to be associated with the nucleation of martensite plates inside the austenite parent phase. Associated variants are similar to sub-domains inside a thermodynamic system following the statistical definition of [37]. The germination process of a variant is on the other hand dictated by a germination potential barrier identified from a differential scanning calorimetry (DSC) measurement. The stochastic average for each type of variants inside a grain leads then to the numerical simulation of hysteresis phenomena at the single crystal scale. Homogenization operations allow finally macroscopic quantities (phases fraction, deformation and temperature) to be calculated at the polycrystalline scale. This procedure is applied to model the whole thermomechanical behavior of an equiatomic Ni-Ti SMA polycrystalline alloy considering the phase transformation between austenite, R-phase and martensite.
Blind diffuser-modulation ptychography has emerged as a low-cost technique for micro–nano holographic imaging, which enables breaking the resolution limit of optical systems. However, the existing reconstruction method requires thousands of measurements to recover object and diffuser profile simultaneously, which makes the data acquisition time-consuming and cumbersome. In this Letter, we report a novel, to the best of our knowledge, blind ptychography technique with deep distributed optimization, termed BPD2O. It decomposes the complicated optimization task into subproblems, then introduces extended ptychographical iterative engine and enhanced network solver to optimize each in a distributed strategy. In this way, BPD2O combines the advantages of both model-driven and data-driven strategies, realizing high-fidelity robust ptychography imaging. Extensive experiments validate that BPD2O can realize better resolution and lead to a reduction of more than one order of magnitude in the number of measurements.
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