Specular Components Research Articles

Aided Imaging Phase Measuring Deflectometry Based on Concave Focusing Mirror

With the rapid development of aerospace, high-speed train, and automotive industries, the demand for the measurement of high-precision specular components is increasing. The acquisition of high-precision three-dimensional (3D) data is conducive to improving the performance of and extending the service life of these components. However, the existing 3D measurement methods of specular surfaces are affected by the inherent limitation of the depth of field (DOF) of camera lenses. Based on the principle of paraxial reflection imaging of a concave mirror, this paper introduces a concave mirror into a phase measuring deflectometry (PMD) system and proposes an aided imaging PMD (AIPMD) based on a concave focusing mirror. The proposed system realizes the clear imaging of the encoded patterns and the surface under test in the DOF of the camera lens, simultaneously. Meanwhile, the iterative coefficient specular reconstruction algorithm is studied based on this system. The feasibility and accuracy of the proposed method are verified by simulations and experiments.

Modeling Weakly Scattering Random Media: A Tool to Resolve the Internal Structure of Nanoporous Materials

Nanoporous media scatter a small fraction of the light propagating through them, even if pore sizes are significantly smaller than the characteristic visible wavelengths. The disordered spatial modulation of the refractive index at the few or few tens of nanometers length scale, resulting from the presence of randomly distributed air bubbles or solid aggregates within a continuous solid background, gives rise to these weak scattering effects. However, standard theoretical approaches to describe this kind of media use effective medium approximations that do not account for diffuse, ballistic, and specular components. Herein, all spectral components and the angular distribution of the scattered light are captured through optical modeling. A Monte Carlo approach, combining scattering Mie theory and Fresnel equations, implemented within a genetic algorithm, allows us to decode the void and aggregate size distribution and hence the internal structure of a nanocrystalline titania (TiO2) film chosen as a paradigmatic example. The approach allows to generically describe the scattering properties of nanoporous materials which, as shown herein, may be used to decipher their internal structure from the fitting of their far‐optical field properties.

Structured channel covariance estimation from limited samples for large antenna arrays

In massive multiuser multiple antenna systems, the knowledge of the users’ channel covariance matrix is crucial for minimum mean square error channel estimation in the uplink and it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters, we propose two methods, where the first solves a nonnegative least-squares problem and the second maximizes the likelihood function using expectation–maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.

Contrast analysis in ultrafast ultrasound blood flow imaging of jugular vein

PurposeThe contrasts of flowing blood in in vitro experiments using porcine blood and in vivo measurements of human jugular veins were analyzed to demonstrate that the hemorheological property was dependent on the shear rate.MethodsBlood samples (45% hematocrit) suspended in saline or plasma were compared with examine the difference in viscoelasticity. Ultrafast plane-wave imaging at an ultrasonic center frequency of 7.5 MHz was performed on different steady flows in a graphite-agar phantom. Also, in vivo measurement was performed in young, healthy subjects and patients with diabetes. A spatiotemporal matrix of beamformed radio-frequency data was used for the singular value decomposition (SVD) clutter filter. The clutter-filtered B-mode image was calculated as the amplitude envelope normalized at the first frame in the diastolic phase to evaluate contrast. The shear rate was estimated as the velocity gradient perpendicular to the lateral axis.ResultsAlthough nonaggregated erythrocytes at a high shear rate exhibited a low echogenicity, the echogenicity in the plasma sample overall increased due to erythrocyte aggregation at a low shear rate. In addition, the frequency of detection of specular components, defined as components beyond twice the standard deviation of a contrast map obtained from a clutter-filtered B-mode image, increased in the porcine blood at a high shear rate and the venous blood in healthy subjects versus patients with diabetes.ConclusionThe possibility of characterizing hemorheological properties dependent on the shear rate and diabetes condition was indicated using ultrafast plane-wave imaging with an SVD-based clutter filter.

The dominating impacts of Helmholtz-Kohlrausch effect on color-induced glossiness enhancement.

Glossiness can be increased by adding chromatic information to the object images. However, the mechanisms that create color-induced glossiness enhancement are unclear. In this study, we psychophysically measured the glossiness of object images to which various hue chromaticities were added to elucidate the perceptual and image factors that explain the color-induced glossiness enhancement effect. Two types of coloring conditions were tested: the both-colored (BC) condition, in which both specular and diffuse components were colored with the same chromaticity, and the diffuse-colored (DC) condition, in which only diffuse components were colored while specular components remained achromatic. The results showed that glossiness enhancement was more prominent in the BC than in the DC condition, and the dependency of glossiness enhancement on the stimulus color direction was similar to that of the Helmholtz-Kohlrausch (H-K) effect. Furthermore, we performed a regression analysis with a linear mixed model based on image features and an additional experiment in which an H-K effect-based increase in perceived brightness was imitated on achromatic stimuli by manipulating luminance. The results demonstrated that the H-K effect-based brightness enhancement in the highlight regions explains the glossiness enhancement effect well. These results suggest that the H-K effect, especially around the highlight region, is a dominant factor that creates the color-induced glossiness enhancement, although other color-related factors could also be partly involved.

Different Power Adaptive Transmission Schemes Over Alternate Rician Shadowed Fading Channels

The two-sided bimodality property of Alternate Rician Shadowed (ARS) fading channels makes it a suitable fading model for body-centric wireless communication. The ARS fading model consists of two fluctuating specular components in which only one is active at a time. To provide excellent spectral efficiency with higher quality of service, system transmitter employs an adaptive transmission scheme. This paper derives the exact closed-form expressions of the ARS fading channels capacity. Various adaptive transmission schemes such as channel inversion with a fixed-rate, truncated channel inversion with a fixed-rate, and optimal rate adaptation are considered. A comparison of these schemes is also highlighted. Performance expressions such as binary bit error rate for various coherent and non-coherent modulation schemes and the outage probability are also derived. The derived results are plotted and analyzed for varying fading parameters of the ARS channel. Finally, the results of all performance metrics are verified with Monte-Carlo simulation results.

Fast measurement of surface defects on large components with dynamic phase-shifting digital holographic microscopy

The microscopic characterization of the surface defects on large opto-electronics components is essential to ensure the performance of major equipment. However, existing technology cannot achieve efficient and reliable fast cross-scale measurement due to the requirement on vertical scanning or polarization modulation for multi-frame capturing at each position. An effective approach is proposed to solve this problem, which needs to record only one interferometric hologram at each location and achieves full-area measurement by traversing over the surface. The overlapped area between adjacent regions are specified by wavefront registration and the topography in this area is obtained by two-step phase shifting and holographic reconstruction. Experimental results demonstrate that the proposed method can achieve fast and fast measurement of large specular components with high accuracy and high efficiency.

Deep Highlight Removal Using Temporal Dark Prior in High-Speed Domain

This paper proposes a deep highlight removal method based on the dichromatic model under alternating current (AC) light sources with a new diffuse prior on temporal domain, temporal dark prior. An input image is decomposed into specular and diffuse components using a deep network. Due to AC powered lights, both incident and reflected lights are time-varying. We exploit the periodic variation property of the specular and diffuse reflections as a prior for dichromatic model based image decomposition. In addition, we propose a new temporal dark prior as a pseudo-diffuse reflection. Unlike the conventional prior in the spatial domain, to the best of our knowledge, this is the first study to utilize a diffuse prior on the temporal domain for highlight removal. The blurred version of the temporal dark prior is additionally fed to the network to alleviate hole artifacts. It is demonstrated through diverse experiments that these temporal priors can strongly contribute to accurate image decomposition, leading to better highlight removal.

ЕНЕРГЕТИЧНО-КОРЕКТНА МОДЕЛЬ ОСВІТЛЕННЯ, ОСНОВАНА НА РОЗРАХУНКУ КУТА МІЖ ВЕКТОРАМИ

Computer graphics allows to significantly increase the bandwidth of the information channel, through which two-way communication between the user and the computer is created, and therefore the value of graphical presentation of calculation results in industry and research practice is increasing. While creating the three-dimensional graphic images, a great attention is paid to realism, which makes it possible to adequately display objects and processes. During the formation of such images it is important to realistically reproduce colors, the gradation of which creates the effect of volume. During the reproduction the specular component of color the bidirectional reflectance distribution function is used, which shows how much light is reflected from the surface to the observer. The most common are the Blinn and Fong models. Unfortunately, these models do not comply with the law of conservation of energy, which certainly affects the realism of the formation of graphic scenes. The work provides a detailed analysis of the most widespread reflectance models. As a base for the modification, the distribution function is chosen, which uses an angle between the median vector and the normal vector. The article describes the search of the normalized coefficient formula for the reflectance model based on the calculation of the angle between the vectors that approximates the Blinn-Fong model. The features of the approximated model are analyzed. The initial data for finding the normalizing coefficient formula on the selected intervals were calculated. Using the Python gplearn library, a program for selecting the normalizing coefficient formula has been developed. The parameters of the genetic algorithm for selecting formulas have been adjusted. The approximation accuracy of the training set is calculated. A table of absolute errors of the hemispherical integral reflectivity is given. The resulting surface reflectance model can be used in highly realistic computer graphics systems to create three-dimensional scenes.

A Fast Specular Highlight Removal Method for Smooth Liquor Bottle Surface Combined with U2-Net and LaMa Model.

Highlight removal is a critical and challenging problem. In view of the complex highlight phenomenon on the surface of smooth liquor bottles in natural scenes, the traditional highlight removal algorithms cannot semantically disambiguate between all-white or near-white materials and highlights, and the recent highlight removal algorithms based on deep learning lack flexibility in network architecture, have network training difficulties and have insufficient object applicability. As a result, they cannot accurately locate and remove highlights in the face of some small sample highlight datasets with strong pertinence, which reduces the performance of some tasks. Therefore, this paper proposes a fast highlight removal method combining U2-Net and LaMa. The method consists of two stages. In the first stage, the U2-Net network is used to detect the specular reflection component in the liquor bottle input image and generate the mask map for the highlight area in batches. In the second stage, the liquor bottle input image and the mask map generated by the U2-Net are input to the LaMa network, and the surface highlights of the smooth liquor bottle are removed by relying on the powerful image inpainting performance of LaMa. Experiments on our self-made liquor bottle surface highlight dataset showed that this method outperformed other advanced methods in highlight detection and removal.

AvatarMe++: Facial Shape and BRDF Inference With Photorealistic Rendering-Aware GANs.

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image. Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce the first method that is able to reconstruct photorealistic render-ready 3D facial geometry and BRDF from a single "in-the-wild" image. To achieve this, we capture a large dataset of facial shape and reflectance, which we have made public. Moreover, we define a fast and photorealistic differentiable rendering methodology with accurate facial skin diffuse and specular reflection, self-occlusion and subsurface scattering approximation. With this, we train a network that disentangles the facial diffuse and specular reflectance components from a mesh and texture with baked illumination, scanned or reconstructed with a 3DMM fitting method. As we demonstrate in a series of qualitative and quantitative experiments, our method outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image, that are ready to be rendered in various applications and bridge the uncanny valley.

Exploring the effect of pavement reflection and photometric properties on road lighting performance

The reflection properties of pavement have an impact on the lit environment and thus upon drivers’ vision and comfort and the energy consumption of the lighting installation. The reflection properties combine diffuse and specular components. The specular reflection component changes with different materials: it also changes when the surface is wet, although this is sometimes ignored in lighting design. This study used simulation (DIALux 4.13) to investigate the effect on lighting parameters (luminance, overall uniformity, longitudinal uniformity and threshold increment (TI)) of changes in pavement reflection properties using different pavement materials and under dry and wet conditions. Furthermore, comparison of lighting parameters was made with different road lighting arrangements. The results show that an increase in the specular reflection component leads to an increase in luminance and a decrease in uniformity. Of the surfaces investigated, the porous pavement had the lower luminance but better uniformity. Arranging the lighting installation based on the extreme wettest condition could make luminance and uniformity rise but with an increase of 2–2.5 kWh/m2 annual energy consumption. When trying to control glare problems during design process, it is suggested that uniformity cannot be neglected except the TI.

The perception of translucency from surface gloss

Translucent objects (like fruit and wax) reflect and transmit incident light to generate complex retinal image structure. Understanding how we visually perceive translucency from these images is challenging, but previous studies have demonstrated that perceived shape and shading is important for perceiving translucency. We considered the possibility that perceived translucency might also depend on 3D shape inferred from surface gloss (i.e., shape from specular highlights). Here, we performed experiments to test whether interactions between specular and non-specular image properties generated by different 3D shape information influences perceived translucency. Results revealed that perceived translucency could be explained by incongruence in 3D shape used to generate specular and non-specular image components. We proposed a new computational model based on measurable image features informative of shading relative to specular highlights that accounted for 59% of the variability in judgments of perceived translucency from the result of 10-fold cross validation. This model was found to outperform other models based on explicit subjective measures of perceived surface shape, suggesting it implicitly taps much of the relevant geometric information necessary for predicting observer judgments of translucency for glossy materials. These results provide new insight into how the visual system might infer translucency from the structure of specular and non-specular shading generated by glossy semi-opaque materials.

Appearance estimation and reconstruction of glossy object surfaces based on the dichromatic reflection model

AbstractWe propose an approach for estimating and reconstructing the material appearance of objects based on the spectral image data acquired in complex illumination environments with multiple light sources. The object appearance can be constructed with various material properties such as spectral reflectance, glossiness, and matteness under different geometric and spectral illumination conditions. The objects are assumed to be made of an inhomogeneous dielectric material with gloss or specularity. The color signals from the object surface are described by the standard dichromatic reflection model, which consists of diffuse and specular reflections, where the specular component has the same spectral composition as the illuminant. The overall appearance of objects is determined by a combination of chromatic factors, based on the reflectance and illuminant spectra, and shading terms, which represent the surface geometries of the surface illumination. Therefore, the appearance of a novel object can be reconstructed by modifying the chromatic factors and shading terms. The method for appearance estimation and reconstruction comprises four steps: (1) illuminant estimation, (2) spectral reflectance estimation, (3) shading term estimation and region segmentation, and (4) appearance reconstruction based on the reflection model. The proposed approach is validated in an experiment in which objects of different materials are illuminated using different light sources. We demonstrate typical reconstruction results with novel object appearances.

ReDDLE-Net: Reflectance Decomposition for Directional Light Estimation

The surfaces of real objects can visually appear to be glossy, matte, or anywhere in between, but essentially, they display varying degrees of diffuse and specular reflectance. Diffuse and specular reflectance provides different clues for light estimation. However, few methods simultaneously consider the contributions of diffuse and specular reflectance for light estimation. To this end, we propose ReDDLE-Net, which performs Reflectance Decomposition for Directional Light Estimation. The primary idea is to take advantage of diffuse and specular clues and adaptively balance the contributions of estimated diffuse and specular components for light estimation. Our method achieves a superior performance advantage over state-of-the-art directional light estimation methods on the DiLiGenT benchmark. Meanwhile, the proposed ReDDLE-Net can be combined with existing calibrated photometric stereo methods to handle uncalibrated photometric stereo tasks and achieve state-of-the-art performance.

Highlight Removal of Multi-View Facial Images.

Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction.

Passive Terahertz Non-Line-of-Sight Imaging

We demonstrate the capability of the THz frequency (submillimeter wavelength) band to perform nonline-of-sight imaging by reflection from common rough surfaces. Active bidirectional reflectance distribution function measurements at 336 GHz show that surface roughness affects the strength, but not the width, of a specularly reflected beam of submillimeter-wavelength light. Uncooled passive direct-detection receivers built using high-frequency InP transistor technology achieve noise-equivalent temperature differences (NETD) ≤ 33 mK. One and two-dimensional, scanned single-pixel images made using such a passive receiver show that determination of the location, orientation, and pose of human targets can easily be made on surfaces whose specular component of reflectance is as low as 8%, without any algorithmic assistance. With such low instrumental NETD levels, the radiometric component of image quality is dominated by real fluctuations in the radiometric temperature of ordinary indoor backgrounds.

Multispectral LiDAR point cloud highlight removal based on color information.

With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color point clouds still faces many challenges. In this paper, a highlight removal method for MSL color point clouds is proposed to explore the potential of 3D visualization. First, the MSL reflection model are introduced according to radar equation and Phong model, and the restored color of the MSL point clouds is determined to comprise diffuse and specular components. Second, a data conversion method is proposed to improve the massive point cloud processing efficiency by spatial dimension reduction and data compression. Then, the visual saliency map after color denoising is used to obtain the highlight region, the unknown information of which is recovered based on the global or local color information. Finally, three representative targets are selected and evaluated by qualitative and quantitative validation, which verifies that the method can effectively recover the high-quality highlight-free point clouds of MSL.

Sparse ellipsometry

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry , a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects.

Identifying specular highlights: Insights from deep learning.

Specular highlights are the most important image feature for surface gloss perception. Yet, recognizing whether a bright patch in an image is due to specular reflection or some other cause (e.g., texture marking) is challenging, and it remains unclear how the visual system reliably identifies highlights. There is currently no image-computable model that emulates human highlight identification, so here we sought to develop a neural network that reproduces observers’ characteristic successes and failures. We rendered 179,085 images of glossy, undulating, textured surfaces. Given such images as input, a feedforward convolutional neural network was trained to output an image containing only the specular reflectance component. Participants viewed such images and reported whether or not specific pixels were highlights. The queried pixels were carefully selected to distinguish between ground truth and a simple thresholding of image intensity. The neural network outperformed the simple thresholding model—and ground truth—at predicting human responses. We then used a genetic algorithm to selectively delete connections within the neural network to identify variants of the network that approximated human judgments even more closely. The best resulting network shared 68% of the variance with human judgments—more than the unpruned network. As a first step toward interpreting the network, we then used representational similarity analysis to compare its inner representations to a wide variety of hand-engineered image features. We find that the network learns representations that are similar not only to directly image-computable predictors but also to more complex predictors such as intrinsic or geometric factors, as well as some indications of photo-geometrical constraints learned by the network. However, our network fails to replicate human response patterns to violations of photo-geometric constraints (rotated highlights) as described by other authors.