In real-world applications, the ability to solve calibrated photometric stereo with a limited number of lights is highly valued. Recognizing the strengths of neural networks in material appearance processing, this paper presents a bidirectional reflectance distribution function (BRDF) model. This model leverages reflectance maps obtained from a limited selection of light sources and can accommodate diverse BRDF structures. In the pursuit of optimal computation methods for BRDF-based photometric stereo maps, considering shape, size, and resolution, we conduct experimental analysis to understand their contribution to normal map estimation. Through analysis of the training dataset, the necessary BRDF data was identified for the application between the measured and parametric BRDFs. The suggested approach was placed under the microscope against the most up-to-date photometric stereo algorithms for a range of data, encompassing simulations, the DiliGenT dataset, and recordings from our two acquisition setups. Observation maps are outperformed by our representation, as a BRDF for neural networks, in the results, demonstrating this improvement across various surface appearances, from specular to diffuse.
We present a novel, objective method for anticipating visual acuity trends from through-focus curves generated by specific optical components, which we subsequently implement and validate. Sinusoidal grating imaging, accomplished with optical elements, served as the basis for the proposed method's acuity definition. Using a custom-designed monocular visual simulator, possessing active optics, the objective method was implemented and its efficacy was established through subjective assessments. From six subjects experiencing paralyzed accommodation, monocular visual acuity was determined using an uncorrected naked eye, followed by compensation with four multifocal optical elements applied to that eye. Predicting the trends of the visual acuity through-focus curve for all considered cases, the objective methodology proves effective. For all the optical elements tested, the Pearson correlation coefficient demonstrated a value of 0.878, aligning with the results of similar investigations. For optical element evaluation in ophthalmic and optometric contexts, the proposed technique offers an alternative that is simple, direct, and easily implemented, allowing testing before potentially invasive, demanding, or expensive procedures on real subjects.
Recent decades have seen the employment of functional near-infrared spectroscopy to detect and measure variations in hemoglobin levels within the human brain. The noninvasive technique offers insights into brain cortex activation correlated with distinct motor/cognitive tasks or external stimulations. Frequently, the human head is modeled as a homogeneous medium, yet this simplification disregards the head's intricate layered structure, consequently causing extracranial signals to mask cortical signals. This work's approach to reconstructing absorption changes in layered media involves the consideration of layered models of the human head during the process. For this purpose, analytically determined average photon path lengths are employed, ensuring swift and straightforward implementation within real-time applications. Synthetic data generated by Monte Carlo simulations in turbid media composed of two and four layers indicate that a layered model of the human head demonstrably outperforms homogeneous models. Two-layer models show errors contained within 20%, but four-layer models typically display errors greater than 75%. Measurements of dynamic phantoms, conducted experimentally, support this conclusion.
The quantification of spectral imaging information along both spatial and spectral axes, using discrete voxels, results in a 3D spectral data cube structure. Darovasertib Spectral images (SIs) are instrumental in the recognition of objects, crops, and materials within a scene based on their corresponding spectral behavior. The limitation of most spectral optical systems to 1D or a maximum of 2D sensors makes directly acquiring 3D information from commercially available sensors challenging. Darovasertib As an alternative to other methods, computational spectral imaging (CSI) enables the acquisition of 3D data through a process involving 2D encoded projections. Afterwards, a computational recovery mechanism must be implemented to retrieve the SI. Compared to conventional scanning systems, CSI-enabled snapshot optical systems achieve reduced acquisition times and lower computational storage costs. Deep learning (DL) advancements have enabled the creation of data-driven CSI systems, enhancing SI reconstruction and enabling advanced tasks like classification, unmixing, and anomaly detection directly from 2D encoded projections. This work's summation of CSI advancements begins with SI and its relation, and then moves to highlight the most crucial compressive spectral optical systems. Finally, this section will introduce CSI with Deep Learning alongside a review of the latest progress in merging physical optical design with Deep Learning algorithms to tackle intricate problems.
The photoelastic dispersion coefficient signifies the link between stress and the disparity in refractive indices within a birefringent material. Nonetheless, the process of pinpointing the coefficient via photoelasticity presents a formidable challenge, stemming from the intricate difficulty in ascertaining the refractive indices of photoelastic materials subjected to tensile stress. This work, to our knowledge, first applies polarized digital holography to investigate the wavelength dependence of the dispersion coefficient in a photoelastic material. A proposed digital method analyzes and correlates the differences in mean external stress with the differences in mean phase. The results unequivocally demonstrate the wavelength dependence of the dispersion coefficient, improving accuracy by 25% compared to other photoelasticity methods.
Laguerre-Gaussian (LG) beams are identified by their azimuthal index, or topological charge (m), which corresponds to the orbital angular momentum, and by their radial index (p), representing the count of rings in the intensity profile. A systematic, in-depth study of the first-order phase statistics in speckle fields generated by the interference of Laguerre-Gauss beams of different orders with random phase screens of variable optical roughness is performed. Applying the equiprobability density ellipse formalism, the phase properties of LG speckle fields are studied in both the Fresnel and Fraunhofer regimes, yielding analytically derived expressions for phase statistics.
By leveraging polarized scattered light, Fourier transform infrared (FTIR) spectroscopy enables the measurement of absorbance in highly scattering materials, a technique that overcomes the challenges posed by multiple scattering. Biomedical applications in vivo and agricultural/environmental monitoring in the field have been documented. This paper details a polarized light microelectromechanical systems (MEMS)-based Fourier Transform Infrared (FTIR) spectrometer operating in the extended near-infrared (NIR) region. The system incorporates a bistable polarizer within a diffuse reflectance measurement configuration. Darovasertib The uppermost layer's single backscattering and the deep layers' multiple scattering can be differentiated by the spectrometer. The spectrometer's spectral range extends from 1300 nm to 2300 nm (4347 cm⁻¹ to 7692 cm⁻¹), and it achieves a spectral resolution of 64 cm⁻¹ (approximately 16 nm at a wavelength of 1550 nm). A core element of the technique is the normalization of the MEMS spectrometer's polarization response. This procedure was applied to milk powder, sugar, and flour, each placed in plastic bags. Particles exhibiting different scattering sizes serve as the basis for evaluating the technique. The range of diameters for the scattering particles is expected to be between 10 meters and 400 meters. The absorbance spectra of the samples, when extracted, exhibit a strong correlation with direct diffuse reflectance measurements, resulting in a satisfactory agreement. At a wavelength of 1935 nm, the error in flour calculation diminished from an initial 432% to a more accurate 29%, thanks to the proposed technique. The wavelength error's influence is further mitigated.
Recent data reveal that 58% of chronic kidney disease (CKD) patients exhibit moderate to advanced periodontitis, a condition triggered by adjustments in the saliva's pH and chemical composition. In truth, the formulation of this vital bodily substance could be swayed by systemic illnesses. We scrutinize the micro-reflectance Fourier-transform infrared spectroscopy (FTIR) spectra of saliva collected from CKD patients undergoing periodontal therapy. The aim is to discover spectral markers indicative of kidney disease progression and the effectiveness of periodontal treatment, hypothesizing potential biomarkers for disease evolution. Saliva samples from 24 stage 5 chronic kidney disease male patients, aged 29 to 64, were examined at (i) the initiation of periodontal care, (ii) 30 days following periodontal care, and (iii) 90 days after periodontal treatment. Following 30 and 90 days of periodontal therapy, statistically important changes were detected across the groups, considering the broad fingerprint region (800-1800cm-1). The predictive power of certain bands was evident (AUC > 0.70), specifically those related to poly (ADP-ribose) polymerase (PARP) conjugated DNA at 883, 1031, and 1060cm-1, along with carbohydrates at 1043 and 1049cm-1 and triglycerides at 1461cm-1. A noteworthy finding in analyzing derivative spectra in the 1590-1700cm-1 secondary structure region was the over-expression of -sheet structures after 90 days of periodontal treatment. This could be potentially correlated with a corresponding rise in human B-defensin levels. Conclusive evidence of PARP detection is supported by the observation of conformational alterations in the ribose sugar within this designated section.