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Inverse problems in imaging are typically ill-posed and are solved using regularized optimization techniques or - in recent times, by employing deep neural networks. While the deep network method enables fast end-to-end reconstruction from raw measurements, it does not necessarily alter the fundamentally ill-posed nature of the underlying inverse problem. It is well known that diverse, non-redundant measurements can improve the robustness of reconstruction algorithms. However, acquiring multiple measurements typically - involves additional hardware and more complex system setups, that may not always be desirable for field deployment. In this work, we note that in both incoherent and coherent (phase retrieval) optical imaging, the irradiance patterns corresponding to two phase diverse measurements associated with the same test object have implicit local correlations, which may be learned by a suitable deep network. A physics informed data augmentation scheme is then described where a trained network is used for generating a phase diverse pseudo-data based on a ground truth data frame, typically acquired using a standard imaging system. We validate this data augmentation approach for both incoherent and coherent optical imaging - configurations, with vortex phase as a - diversity mechanism. Specifically, we observe that the generated pseudo-data closely match the corresponding ground-truth data, with comparable noise characteristics. We further demonstrate that the true data along with the augmented pseudo-data provide- high quality inverse solutions with simpler robust reconstruction algorithms. Our results may open new avenues for leaner, high-fidelity computational imaging systems across a broad range of applications.

The piston error has a significant effect on the imaging resolution of the optical sparse aperture system. In this paper, an improved phase diversity method based on particle swarm optimization and the sequential quadratic programming algorithm is proposed, which can overcome the drawbacks of the traditional phase diversity method and particle swarm optimization, such as the instability that results from polychromatic light conditions and premature convergence. The method introduces factor β in the stage of calculating the objective function, and combines the advantages of a heuristic algorithm and a nonlinear programming algorithm in the optimization stage, thus enhancing the accuracy and stability of piston detection. Simulations based on a dual-aperture optical sparse aperture system verified that the root mean square error obtained by the method can be guaranteed to be within 0.001λ (wavelength), which satisfies the requirement of practical imaging. An experimental test was also conducted to demonstrate the performance of the method, and the test results showed that the quality of the image after piston detection and correction improved significantly compared to images with the co-phase error.

Although tidal energy conversion technologies are not yet commercially available or cost-competitive with other renewable energy technologies like wind turbines and solar panels, tides are a highly predictable resource. Tidal energy’s predictability indicates that the resource could introduce less volatility into balancing the electric grid when compared to other renewables, a fundamentally desirable attribute for the electric system. More specifically, tidal energy resources are unique in that they have the potential to produce relatively smoother power profiles over time through aggregation. In order to generate smooth power profiles from tidal resources, sufficient complexity within the timing of tides is necessary within electrical proximity. This study evaluates the concept of aggregating diverse tides for the purpose of reducing periods of no and low energy production and creating smoother power profiles in regions around Alaska and Washington by calculating cross-correlations of tidal current velocity time series. Ultimately, study results show limited potential to exploit the resources for this purpose and describe the institutional mechanisms necessary to realize the benefits in practice.

A variety of nonlinear circuits can be tamed to reproduce the main dynamical properties in neural activities for biological neurons while designing reliable artificial synapses for connecting these neural circuits becomes a challenge. In this paper, a Josephson junction is used to build coupling channel for connecting two FitzHugh-Nagumo neural circuits driven by periodical voltage source. The hybrid synapse is designed by using a linear resistor paralleled with a Josephson junction, which can estimate the effect of external magnetic field by generating additive phase error between the junction. Indeed, it activates nonlinear coupling due to phase diversity in the Josephson junction. The coupled circuits are estimated in dimensionless dynamical systems by applying scale transformation on the variables and parameters in neural circuits. In fact, the intrinsic parameters of coupling channel are adjusted to detect the occurrence of synchronization. Bifurcation analysis is calculated to predict and confirm the occurrence of synchronization between two neural circuits. It is found that synchronization can be stabilized between two FHN neural circuits by selecting appropriate values for parameters in the Josephson junction involved in the coupling channel. It gives useful guidance for implementing artificial synapse for signal processing in neural circuits.

Due to complex thermodynamic and kinetic mechanism, phase engineering in nanomaterials is often limited by restricted phases and small-scale synthesis, hindering material diversity and scalability. Here, we demonstrate the exploration to unlock the stoichiometry as a degree of freedom for phase engineering in the Pd-Te binary compound. By reducing diffusion rates, we effectively engineer the stoichiometry of the reactants. We visualize the kinetic process, showing the stoichiometry transition from Pd 10 Te 3 to PdTe 2 through a sequential multi-step nucleation process. In total, five distinct phases are identified, demonstrating the potential to enhance phase diversity by fine-tuning stoichiometry. By controlling spatially uniform nucleation and halting the phase transition at precise points, we achieve stoichiometry-controllable wafer-scale growth. Notably, four of these phases exhibit superconducting properties. Our findings offer insights into the mechanism of phase transition through stoichiometry engineering, enabling the expansion of the phase library in nanomaterials and advancing scalable applications. The precise control and transition between multiple stochiometric phases is challenging. Here, the authors grow and characterize four wafer-scale stoichiometric phases of a Pd-Te binary compound through a sequential multi-step nucleation process.

We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach.

Phase diversity techniques commonly employ a pair of focused–defocused images to retrieve the incident wave front and to restore the observed scene. However, the combination of more images, each one affected by a different amount of defocus, has been barely explored in solar astronomy. In this work we reformulate the “classic” two-images phase diversity approach to accommodate an arbitrary number of phase differences and we investigate its performance in synthetic magnetohydrodynamical simulations of the solar scene corrupted by noise and degraded by a certain set of aberrations. We employ different combinations of images defocused from ±0.5 λ up to ±2 λ (peak to peak) and compare both the retrieved wave front with the incident one and the restored images with the unaberrated noiseless scene. We investigate the effect of using a series of images defocused both symmetrically and asymmetrically with respect to the focused one. In these two cases the performance of the method is improved with the use of more than two images, although it benefits more from the use of symmetric defocuses. We find also that there is a qualitative best choice of the number of phase diversity images in terms of the goodness of the wave front retrieval and of the restored object. The presented method has a potential use either in instruments equipped with a refocusing mechanism or during the laboratory calibrations of the instrument provided that an optical target can be defocused manually by different amounts.

Laser phase retrieval plays a crucial role in the evaluation of laser beam quality, yet model-based approaches are often limited by their sensitivity to initial conditions and susceptibility to local minima. To address these challenges, we propose AttentionPD-ResUNet, a phase retrieval framework that integrates the Phase Diversity (PD) method with attention mechanisms. Specifically, focused and defocused intensity images acquired via PD are employed as inputs to the network, which incorporates SE channel recalibration, ASPP-based multi-scale sampling, and spatial attention modules. This design enables the establishment of an end-to-end nonlinear mapping from the measured intensity distributions to the underlying wavefront phase. In comparative experiments, the proposed method achieves an RMSE of 0.068 and an MAE of 0.041 relative to the ground truth, with an average inference time of 0.41 s, thereby presenting a promising approach for reliable laser beam quality assessment.

Bandwidth and noise are fundamental considerations in all communication and signal processing systems. The group-velocity dispersion of optical fibers creates nulls in their frequency response, limiting the bandwidth and hence the temporal response of communication and signal processing systems. Intensity noise is often the dominant optical noise source for semiconductor lasers in data communication. In this paper, we propose and demonstrate a class of electrooptic modulators that is capable of mitigating both of these problems. The modulator, fabricated in thin-film lithium niobate, simultaneously achieves phase diversity and differential operations. The former compensates for the fiber’s dispersion penalty, while the latter overcomes intensity noise and other common mode fluctuations. Applications of the so-called four-phase electrooptic modulator in time-stretch data acquisition and in optical communication are demonstrated. In this work, the authors showcase four-phase electrooptic modulators (FEOMs) implemented on thin-film lithium niobate. This innovation effectively addresses challenges related to dispersion and semiconductor laser noise limitations, offering a promising solution for integrated photonic applications.

Phase diversity techniques are usually based on the comparison of synchronously acquired pairs of focused–defocused images. This way, differences between both images are avoided except from random pixel variations due to noise on the detector and the phase diversity itself. In some astronomical instruments, though, the two images are not taken simultaneously. This work studies the impact of carrying out phase diversity with pairs of asynchronously acquired images while observing an evolving solar scene. We evaluate the performance of this technique as a function of the time gap between the images through the use of a magnetohydrodynamical simulation of the solar scene as observed by an instrument. We describe the incident wave front with two numbers of Zernike polynomials (20 or 32) to explore their effect on the wave front sensing accuracy and we employ two levels of noise to study their impact in the object restoration. We find that a time gap among our simulation images smaller than ∼10 s has a negligible impact on the performance of the method. The rms error of the Zernike coefficients fitting worsens exponentially from there on, but the evolution is similar no matter the number of polynomials used in the fitting. Meanwhile, the quality of the object restoration benefits from lower noise levels, but it decreases linearly with the time gap independently of the amount of noise.