Journal of The Optical Society of America A-optics Image Science and Vision最新文献

Ultrasound-modulated diffuse optical tomography (US-DOT) is an emerging imaging modality where spatial distributions of optical parameters of an imaged target are estimated from measured near-infrared light modulated by a focused ultrasound through an acousto-optic effect. The use of acoustic modulation enables increased resolution when compared to conventional diffuse optical tomography. Generally, estimation methods in US-DOT have assumed stationary tissue properties, limiting their capability to capture temporal variations of the unknown optical parameters within biological tissues that can occur, for example, due to the time needed to scan with multiple ultrasound focuses. In this work, we aim to estimate the dynamic optical parameters of tissues by approaching the image reconstruction problem of US-DOT in the Bayesian framework for non-stationary estimation. We implemented two time-varying models: a random walk and a vector autoregression model. The approach was evaluated with numerical simulations for the difference (linear) and absolute (non-linear) imaging of absorption coefficients. The results were compared to a conventional approach where the state estimation model was not used. The results demonstrate that state estimation significantly improves the reconstruction of non-stationary targets in US-DOT, even if relatively simple time-evolution models are used.

In this paper, we present the results of an experimental analysis of atmospheric optical turbulence dynamics during the 2024 total solar eclipse (TSE). Changes in atmospheric optical turbulence strength throughout the entire TSE event were investigated by measuring the refractive index structure parameter (Cn2) and analyzing the corresponding short-exposure intensity scintillation patterns of a laser beam propagating over a 7 km near-ground atmospheric path. Data collection was conducted using a commercial large-aperture scintillometer and the recently developed AI-based TurbNet sensor, which enables high temporal resolution for Cn2 evaluation. The observed Cn2 parameter time evolution was characterized by the presence of dual turbulence quiescent (neutral stratification) phases and the generation of quasi-periodic oscillatory patterns during totality and for several minutes afterward.

To solve the problem of poor depth estimation due to the influence of occlusion in light-field imaging systems, an embeddable adaptive occlusion-aware module (AOAM) is proposed to effectively compensate for the deficiencies of most existing frameworks. Considering the low computational resource consumption, an adaptive occlusion optimization mode is built that introduces a voting strategy. The beam propagation characteristics are analyzed to filter the disparity values, and the adaptive voting cost is utilized to achieve regional partitioning and noise reduction in the global domain. The superiority of the proposed method is validated on a common light-field dataset.

RAW images are unprocessed camera sensor output with sensor-specific RGB values based on the sensor's color filter spectral sensitivities. RAW images also incur strong color casts due to the sensor's response to the spectral properties of scene illumination. The sensor- and illumination-specific nature of RAW images makes it challenging to capture RAW datasets for deep learning methods, as scenes need to be captured for each sensor and under a wide range of illumination. Methods for illumination augmentation for a given sensor and the ability to map RAW images between sensors are important for reducing the burden of data capture. To explore this problem, we introduce what we believe to be a first-of-its-kind dataset comprising carefully captured scenes under a wide range of illumination. Specifically, we use a customized lightbox with tunable illumination spectra to capture several scenes with different cameras. Our illumination and sensor mapping dataset has 390 illuminations, four cameras, and 18 scenes. Using this dataset, we introduce a lightweight neural network approach for illumination and sensor mapping that outperforms competing methods. We demonstrate the utility of our approach on the downstream task of training a neural ISP.

The lens paradox refers to the phenomenon where the human lens loses refractive power, despite the increase in external curvature and thickness associated with aging. In this study, we develop an age-dependent GRINCU (gradient index and gradient curvature of the iso-indicial surfaces) lens model and propose a quantitative explanation for the lens paradox. Drawing on various sources in the literature, we configured a lens with an age-dependent geometry and implemented this model for an age range of 23-73 years. By adjusting the internal curvature gradient for each age, we optimized the lens refractive power to match experimental measurements within this age range. To compare the results obtained by varying the curvature gradient of the iso-indicial surfaces (IISs), we also modeled a concentric configuration for each age. Among the two configurations, only the model with a variable curvature gradient successfully replicated the decline in refractive power associated with aging, whereas the concentric configuration showed an increasing trend in power with age. To accurately explain the lens paradox, it is crucial to consider not only the changes in refractive index with aging but also the changes in the internal curvature gradient of the lens.

One-dimensional (1D) inverse profiling aims to reconstruct the dielectric profile of 1D scatterers from the scattered field data. While the problem can be significantly simplified within a linearized framework (albeit at the cost of obvious performance limitations), the solution remains challenging due to the inherent ill-posedness of the problem, which is related to the "filtering" introduced by the linear scattering operator. To improve reconstruction accuracy, a host medium with known inhomogeneities can be exploited. Such a medium enriches the scattering environment by introducing additional reflections and scattering, which alters the properties of the scattering operator, resulting, in general, in less severe filtering. In this study, we investigate the effect of a perfect reflecting plane on the 1D linear inverse profiling problem using multi-frequency data. While this scenario has previously been explored through somewhat qualitative studies, we focus on the mathematical features of the involved scattering operator via its singular value decomposition (SVD). In particular, we derive an analytical estimation of the singular system, which in turn allows us to identify the influence of the configuration parameters on the achievable performance. In more detail, the point-spread function (PSF) can be analytically determined and linked to the configuration parameters. Results obtained by using a truncated-SVD regularized inversion scheme show that the reflecting plane doubles the band, with respect to the homogeneous host medium case, of the band-pass filtering that the unknown undergoes during the reconstruction process. Moreover, the presence of the reflecting plane facilitates the reconstruction of the continuous component (mean value) of the unknown dielectric profile as well. Overall, the inclusion of a reflecting plane enables the stable reconstruction of a broader class of dielectric profiles, improving both the resolution and the accuracy of the inverse profiling process.

This study investigates the strong robustness of perfect Laguerre-Gaussian (PLG) beams in orbital angular momentum (OAM) multiplexing systems compared to Laguerre-Gaussian (LG) beams. We demonstrate that PLG beams, with an optimal beam waist of approximately 0.07 m, exhibit superior turbulence resistance properties. Specifically, for the topological charge l of 7 constrained by the characteristics of PLG beams, these beams achieve an increase in received probability of approximately 0.3 over LG beams. Furthermore, multiplexed PLG beams undergo significant phase rotation during propagation, with the rotation direction determined by the sign of the larger topological charge between multiplexed OAM modes. We also demonstrate that PLG beams outperform LG beams in terms of beam integrity, received probability, and bit error rate in OAM multiplexing systems. These findings provide valuable insights into OAM multiplexing and mode recognition, suggesting that multiplexed PLG beams could serve as a promising solution for robust communication in turbulent environments.

Multifocal diffractive optical elements (MFDOEs) as interleaved sawtooth structures with alternating heights are established as single-material layers. While the distribution of diffraction efficiencies across different orders can be selected for a specific wavelength, it is fixed for other wavelengths due to the dispersion of the material. In this work, we investigate multilayer MFDOEs consisting of two layers of sawtooth structures to enable tailored wavelength selectivity, especially the controlled efficiency distribution into specific diffraction orders for selected wavelengths or wavelength ranges. Using scalar diffraction theory, we classify material combinations into four categories based on their dispersion characteristics. This allows for the design of structures that achieve high diffraction efficiencies in either narrow or broadband spectral ranges, directing light into the zeroth, multiple positive, or negative diffraction orders. Using representative material combinations, we evaluate key quantitative measures, including maximum efficiency, wavelength of maximum efficiency, and the full width at half maximum for narrowband efficiency peaks. Broad spectral efficiency maxima are characterized by their average efficiency and root mean square error as a measure of uniformity. These parameters can be adjusted by varying the structure depths, paving the way for wavelength-selective multifocal imaging in microscopy and medical applications.

We present an inverse method for transforming a given parallel light emittance to two light distributions at different parallel target planes using two freeform reflectors. The reflectors control both the spatial and directional target coordinates of light rays. To determine the shape and position of the reflectors, we derive generating functions and use Jacobian equations to find the optical mappings to the two targets. The model is solved numerically by a three-stage least-squares algorithm. A feasibility condition is derived, which ensures that the reflectors are not self-intersecting. Several examples validate this condition and demonstrate the algorithm's capability of realizing complex distributions.

The propagation characteristics of a higher-order annular Gaussian (HOAG) beam in biological tissue turbulence are investigated. Average intensity at the receiver plane is found when the HOAG source field is used as excitation. The effects of the HOAG beam on different tissue types of the upper dermis (human), liver parenchyma (mouse), intestinal epithelium (mouse), and deep dermis (mouse) are studied. Variations of the average intensity versus the source and medium parameters such as the strength coefficient of the refractive-index fluctuations, propagation distance, wavelength, and beam size are presented. The results show that all modes of the HOAG beam can successively transmit beam energy at different levels of turbulence for all tissue types. At the same turbulence strength, HOAG beams having larger mode numbers transmit higher intensity to receivers than the modes with smaller mode orders, which is valid for all the examined tissue types. As the strength of tissue turbulence increases, the HOAG beam slowly turns into a pure Gaussian beam. For the different tissue types, the highest beam intensity at the receiver was observed for the deep dermis (mouse) tissue type. Despite the change in wavelength, refractive-index fluctuations, and source beam size, the highest beam transmission through the tissue in a turbulent environment was also observed for this same tissue type. This research may be useful in understanding the fundamentals of light-tissue interaction of HOAG laser beams, which may improve noninvasive disease detection and therapy methods through tissue in biophotonic technologies.