Intrinsic Reflectance Research Articles

Label-free adaptive optics single-molecule localization microscopy for whole zebrafish

Specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization number owing to severe tissue aberrations.

Confocal reflectance microscopy for mapping collagen fiber organization in the vitreous gel of the eye.

Vitreous collagen structure plays an important role in ocular mechanics. However, capturing this structure with existing vitreous imaging methods is hindered by the loss of sample position and orientation, low resolution, or a small field of view. The objective of this study was to evaluate confocal reflectance microscopy as a solution to these limitations. Intrinsic reflectance avoids staining, and optical sectioning eliminates the requirement for thin sectioning, minimizing processing for optimal preservation of the natural structure. We developed a sample preparation and imaging strategy using ex vivo grossly sectioned porcine eyes. Imaging revealed a network of uniform diameter crossing fibers (1.1 ± 0.3 µm for a typical image) with generally poor alignment (alignment coefficient = 0.40 ± 0.21 for a typical image). To test the utility of our approach for detecting differences in fiber spatial distribution, we imaged eyes every 1 mm along an anterior-posterior axis originating at the limbus and quantified the number of fibers in each image. Fiber density was higher anteriorly near the vitreous base, regardless of the imaging plane. These data demonstrate that confocal reflectance microscopy addresses the previously unmet need for a robust, micron-scale technique to map features of collagen networks in situ across the vitreous.

Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data

AbstractChemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth's land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth's mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo‐photogrammetry at ∼30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo‐photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root‐mean‐square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine‐scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth's mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth's surface.

Invariant descriptors for intrinsic reflectance optimization.

Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill posed and under-constrained, it is a very challenging computer vision problem. There are infinite pairs of reflectance and shading images that can reconstruct the same input. To address the problem, Intrinsic Images in the Wild by Bell et al. provides an optimization framework based on a dense conditional random field (CRF) formulation that considers long-range material relations. We improve upon their model by introducing illumination invariant image descriptors: color ratios. The color ratios and the intrinsic reflectance are both invariant to illumination and thus are highly correlated. Through detailed experiments, we provide ways to inject the color ratios into the dense CRF optimization. Our approach is physics based and learning free and leads to more accurate and robust reflectance decompositions.

An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image

The adjacency effect can blur and reduce the contrast of satellite images. For low visibility, the adjacency effect has a crucial impact on submeter-scale spatial resolution optical (SM) satellite images. Therefore, SM satellite images are preprocessed with an atmospheric correction before performing visual interpretation and quantitative research. This atmospheric correction includes the adjacency effect (expressed by the average background reflectance, ABR) correction and atmospheric intrinsic reflectance correction. ABR is related to the spatial distance from the central pixel to its surrounding pixels (SDCS) and is affected by the reflectance difference between the central pixel and its surrounding pixels (RDCS). Existing methods to calculate ABR do not include the effect of RDCS. Therefore, in this study, we use equivalent average background reflectance (EABR) instead of ABR used in the 6S model. EABR considers the effects of the strengths of the aerosol optical depth (AOD), SDCS, and RDCS on the adjacency effect. Compared with ABR, EABR can better represent the adjacency effect in an SM satellite image. We develop an adaptive atmospheric correction algorithm (called adaptive-AC) based on EABR. The adaptive characteristic of adaptive-AC is that it can adjust the contribution factor (or weighted value) of the background pixel to the adjacency effect according to the SDCS and RDCS. The application of adaptive-AC to a panchromatic band image of WorldView-3 (WV-3) reveals that adaptive-AC can significantly improve image quality, with the reflectance of pixels in the corrected image processed by adaptive-AC being close to the actual reflectance. Comparison with the results of the atmospheric correction algorithm in the 6S model (6S-AC) indicates that adaptive-AC is more suitable for SM satellite image preprocessing than 6S-AC approaches. There is no clear theory or formula available to calculate the exact value of the adjacency effect range (Rad). Therefore, different Rad values are used in applying the adaptive-AC and 6S-AC. The results show that the larger is the Rad used to construct the adjacency effect model, the more satisfactory is the atmospheric correction.

Exploring Chromatic Aberration and Defocus Blur for Relative Depth Estimation From Monocular Hyperspectral Image.

This article investigates spectral chromatic and spatial defocus aberration in a monocular hyperspectral image (HSI) and proposes methods on how these cues can be utilized for relative depth estimation. The main aim of this work is to develop a framework by exploring intrinsic and extrinsic reflectance properties in HSI that can be useful for depth estimation. Depth estimation from a monocular image is a challenging task. An additional level of difficulty is added due to low resolution and noises in hyperspectral data. Our contribution to handling depth estimation in HSI is threefold. Firstly, we propose that change in focus across band images of HSI due to chromatic aberration and band-wise defocus blur can be integrated for depth estimation. Novel methods are developed to estimate sparse depth maps based on different integration models. Secondly, by adopting manifold learning, an effective objective function is developed to combine all sparse depth maps into a final optimized sparse depth map. Lastly, a new dense depth map generation approach is proposed, which extrapolate sparse depth cues by using material-based properties on graph Laplacian. Experimental results show that our methods successfully exploit HSI properties to generate depth cues. We also compare our method with state-of-the-art RGB image-based approaches, which shows that our methods produce better sparse and dense depth maps than those from the benchmark methods.

A modulus-based multigrid method for image retinex

Retinex theory shows that the color of a scene perceived by human visual system is only dependent on the intrinsic reflectance and unrelated to the illumination. For images recorded by digital cameras, the intensities are the product of the scene reflectance and illumination. Image intensities are different when taken under different illuminations. The aim of image retinex problem is to decompose the reflectance from the image intensities. In this paper, we propose a variational model for image retinex problem. The model can be reduced to a linear complementarity problem and a modulus-based multigrid method is applied to solve it. A pointwise coupled modulus-based Gauss-Seidel iteration scheme is used as a smoother of the multigrid method. The smoothing factor of the smoother is analyzed by using the local Fourier analysis. Both theoretical analyses and numerical results show the efficiency of the proposed method for image retinex problem.

Intrinsic Image Recovery From Remote Sensing Hyperspectral Images

In this paper, a novel reflectance model is proposed to recover intrinsic images from remote sensing hyperspectral images (HSIs). Intrinsic image recovery is a well-known challenging and underconstrained problem in computer vision, and it becomes even more severely illposed for HSIs. To reduce the uncertainties and improve the recovery accuracy, two kinds of priors are introduced: 1) shading prior which describes the geometric relation between illuminate and object surface and 2) reflectance prior based on L1-graph coding, which describes the relation between pigment density with reflectance. These priors can effectively eliminate the reflectance inhomogeneity caused by surface normal changes or pigment density variations other than material changes. Then, a noniterative optimization method is proposed to combine the shading prior and reflectance prior, with which closed-form solutions can be derived and thus avoided falling into local optimums. The experimental results demonstrate that the proposed method can efficiently improve the spectral reflectance homogeneity within a class while preserving the image boundaries; it also produces a competitive performance with the state of the art when utilizing the extracted intrinsic hyperspectral reflectance feature in the task of HSI classification.

A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements

When using the kernel-driven bidirectional reflectance distribution function (BRDF) model to process multi-angular measurements, the input multi-angular measurements must be corrected for atmospheric effects. However, in current databases, a significant number of ground-based multi-angular measurements contain either no corrections or only approximate corrections for atmospheric effects. Thus, the blended diffuse light in the total incident irradiance will result in considerable smoothing of the reflectance anisotropy retrieved by the kernel-driven model unless an atmospheric correction process is conducted. In this study, we propose a diffuse-light correction (DLC) form of the kernel-driven model that improves its ability to process multi-angular measurements blended with hemispherical diffuse light. The DLC form of the kernel-driven model can be used to retrieve the intrinsic reflectance anisotropy of the observed target from atmospheric-uncorrected multi-angular measurements. This study used multi-angular data simulated by the PROSAIL and Radiosity Applicable to Porous IndiviDual objects (RAPID) BRDF model, atmospheric-corrected Polarization and Directionality of the Earth's Reflectances (POLDER), Cloud Absorption Radiometer (CAR) multi-angular measurements and their simulated data based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) tools to validate the effectiveness of the DLC form of the kernel-driven model. The results indicated that the reflectance factors directly retrieved by the kernel-driven model are considerably smoothed by the blended diffuse light, especially in hotspot regions. Even under clear and cloudless sky conditions, the retrieved hotspot reflectance in the red band is still underestimated by an average of 9.25%, 7.72%, 11.0% and 13.8% for the PROSAIL, RAPID, POLDER and CAR data, respectively. In contrast, the hotspot reflectance retrieved by the DLC form of the kernel-driven model is very close to the intrinsic reflectance anisotropy of the targets; the average relative error of the DLC form of the kernel-driven model is only 1.99%, 1.50%, 4.57% and 3.42%, respectively. Although the reflectance reconstructed by the DLC form of the kernel-driven model in the hotspot region represents a considerable improvement compared with the reflectance retrieved by the original kernel-driven model, its improvement on the root mean square error (RMSE) and the bias of the entire datasets is not very apparent. Using the DLC form of the kernel-driven model can significantly improve the ability of the kernel-driven model to process multi-angular measurements blended with hemispherical diffuse irradiance.

Development of ReFaST Pyrometer for Measuring Surface Temperature With Unknown Emissivity: Methodology, Implementation, and Validation

The accuracy of temperature measurement by optical pyrometer depends largely on the emissivity configuration. However, determination of emissivity is a hard task as it varies with wavelength, temperature, and physical state of the surface. In this paper, we propose a new type of pyrometer named Reflector with a Fast Switching Tube to measure surface temperature as well as emissivity. The invention is featured by a novel reflector equipped with a fast switching tube inside. The tube can be moved to make two measurements, one is to measure intrinsic radiation from the target, and the other is to measure intrinsic and multiple reflectance radiation; then, the emissivity and temperature are solved out simultaneously. For accuracy validation, we make a new type of thermocouple, namely, flat-type thermocouple and measure the surface of it. By measuring the thermocouple immediately, the temperature drop between the measured surface and the thermocouple can be avoided. The measurement difference is 1.0 °C on average compared with the flat-type thermocouple ranging from 600 °C to 900 °C. The measurement uncertainty is evaluated based on a Monte Carlo method, and the standard uncertainty is 1.4 °C at 750 °C.

Irradiance and duration dependence of the cone photoreceptor intrinsic reflectance response

Irradiance and duration dependence of the cone photoreceptor intrinsic reflectance response

Bidirectional texture function image super-resolution using singular value decomposition.

The bidirectional texture function (BTF) is widely employed to achieve realistic digital reproduction of real-world material appearance. In practice, a BTF measurement device usually does not use high-resolution (HR) cameras in data collection, considering the high equipment cost and huge data space required. The limited image resolution consequently leads to the loss of texture details in BTF data. This paper proposes a fast BTF image super-resolution (SR) algorithm to deal with this issue. The algorithm uses singular value decomposition (SVD) to separate the collected low-resolution (LR) BTF data into intrinsic textures and eigen-apparent bidirectional reflectance distribution functions (eigen-ABRDFs) and then improves the resolution of the intrinsic textures via image SR. The HR BTFs can be finally obtained by fusing the reconstructed HR intrinsic textures with the LR eigen-ABRDFs. Experimental results show that the proposed algorithm outperforms the state-of-the-art single-image SR algorithms in terms of reconstruction accuracy. In addition, thanks to the employment of SVD, the proposed algorithm is computationally efficient and robust to noise corruption.

Intrinsic image estimation using near- $$L_0$$ L 0 sparse optimization

The objective of intrinsic images estimation is to decompose an input image into its intrinsic shading and reflectance components. This is a well-known under-constrained problem that has long been an open challenge. This paper proposes a novel approach for automatic intrinsic images decomposition that uses a new reflectance sparsity prior. On the basis of the observation that the reflectance of natural objects is commonly piecewise constant, we formalize this constraint on the entire reflectance image using the $$L_0$$L0 sparse loss function that enforces the variation in reflectance images to be of high-frequency and sparse. This new sparsity constraint significantly improves the quality of Retinex intrinsic images estimation. It also functions effectively by combining a class of global sparsity priors on reflectance. Experimental results on MIT benchmark dataset as well as various real-world images and synthetic images demonstrate the effectiveness and versatility of our approach.

Correction of terrestrial LiDAR intensity channel using Oren–Nayar reflectance model: An application to lithological differentiation

Ground-based LiDAR has been traditionally used for surveying purposes via 3D point clouds. In addition to XYZ coordinates, an intensity value is also recorded by LiDAR devices. The intensity of the backscattered signal can be a significant source of information for various applications in geosciences.Previous attempts to account for the scattering of the laser signal are usually modelled using a perfect diffuse reflection. Nevertheless, experience on natural outcrops shows that rock surfaces do not behave as perfect diffuse reflectors. The geometry (or relief) of the scanned surfaces plays a major role in the recorded intensity values.Our study proposes a new terrestrial LiDAR intensity correction, which takes into consideration the range, the incidence angle and the geometry of the scanned surfaces. The proposed correction equation combines the classical radar equation for LiDAR with the bidirectional reflectance distribution function of the Oren–Nayar model. It is based on the idea that the surface geometry can be modelled by a relief of multiple micro-facets. This model is constrained by only one tuning parameter: the standard deviation of the slope angle distribution (σslope) of micro-facets.Firstly, a series of tests have been carried out in laboratory conditions on a 2m2 board covered by black/white matte paper (perfect diffuse reflector) and scanned at different ranges and incidence angles. Secondly, other tests were carried out on rock blocks of different lithologies and surface conditions. Those tests demonstrated that the non-perfect diffuse reflectance of rock surfaces can be practically handled by the proposed correction method.Finally, the intensity correction method was applied to a real case study, with two scans of the carbonate rock outcrop of the Dents-du-Midi (Swiss Alps), to improve the lithological identification for geological mapping purposes. After correction, the intensity values are proportional to the intrinsic material reflectance and are independent from range, incidence angle and scanned surface geometry. The corrected intensity values significantly improve the material differentiation.

Empirical model for matching spectrophotometric reflectance of yarn windings and multispectral imaging reflectance of single strands of yarns.

The state-of-the-art multispectral imaging system can directly acquire the reflectance of a single strand of yarn that is impossible for traditional spectrophotometers. Instead, the spectrophotometric reflectance of a yarn winding, which is constituted by yarns wound on a background card, is regarded as the yarn reflectance in textile. While multispectral imaging systems and spectrophotometers can be separately used to acquire the reflectance of a single strand of yarn and corresponding yarn winding, the quantitative relationship between them is not yet known. In this paper, the relationship is established based on models that describe the spectral response of a spectrophotometer to a yarn winding and that of a multispectral imaging system to a single strand of yarn. The reflectance matching function from a single strand of yarn to corresponding yarn winding is derived to be a second degree polynomial function, which coefficients are the solutions of a constrained nonlinear optimization problem. Experiments on 100 pairs of samples show that the proposed approach can reduce the color difference between yarn windings and single strands of yarns from 2.449 to 1.082 CIEDE2000 units. The coefficients of the optimal reflection matching function imply that the reflectance of a yarn winding measured by a spectrophotometer consists of not only the intrinsic reflectance of yarn but also the nonignorable interreflection component between yarns.

Factors affecting the microwave coking of coals and the implications on microwave cavity design

The work carried out in this paper assessed how processing conditions and feedstock affect the quality of the coke produced during microwave coke making. The aim was to gather information that would support the development of an optimised microwave coke making oven. Experiments were carried out in a non-optimised 2450MHz cylindrical cavity. The effect of treatment time (15–120min), power input (750W–4.5kW) and overall power input (1700–27,200kWh/t) on a range of coals (semi-bituminous–anthracite) was investigated. Intrinsic reactivity, random reflectance, strength index and dielectric properties of the produced cokes were compared with those of two commercial cokes to assess the degree of coking produced in the microwave system.Overall energy input and coal rank were found to be the major factors determining the degree of coking following microwave treatment. The dependency on coal rank was attributed to the larger amount of volatiles that had to be removed from the lower ranked coals, and the increasing dielectric loss of the organic component of the coal with rank due to increased structural ordering. Longer treatment times at lower powers or shorter treatment times at higher powers are expected to produce the same degree of coking.It was concluded that microwave coke making represents a potential step-change in the coking industry by reducing treatment times by an order of magnitude, introducing flexibility and potentially decreasing the sensitivity to quality requirement in the feedstock. The main challenges to development are the energy requirements (which will need to be significantly reduced in an optimised process) and penetration depth (which will require an innovative reactor design to maximise the advantage of using microwaves). Understanding and quantifying the rapidly changing dielectric properties of the coal and coke materials is vital in addressing both of these challenges.

Intrinsic Image Decomposition Using Optimization and User Scribbles

In this paper, we present a novel high-quality intrinsic image recovery approach using optimization and user scribbles. Our approach is based on the assumption of color characteristics in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window having similar intensity values should have similar reflectance values. Thus, the intrinsic image decomposition is formulated by minimizing an energy function with the addition of a weighting constraint to the local image properties. In order to improve the intrinsic image decomposition results, we further specify local constraint cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination, and fixed-illumination brushes. Our experimental results demonstrate that the proposed approach achieves a better recovery result of intrinsic reflectance and illumination components than the previous approaches.

Intrinsic Images by Clustering

AbstractDecomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.

In situ reflectance and optical constants of ion-beam-sputtered SiC films in the 584 to 1492 nm region

The reflectance of freshly deposited SiC thin films is measured in situ for what we believe is the first time. SiC was deposited by means of ion-beam sputtering. Reflectance was measured as a function of the incidence angle in the far and extreme ultraviolet wavelengths from 58.4 to 149.2 nm. In situ measurements allowed obtaining the intrinsic reflectance of SiC films, which is somewhat larger than what had been measured for samples exposed to the atmosphere. Reflectance measurements were used to determine the optical constants of the material in the same spectral range. We compare our data to those of the literature corresponding to SiC films deposited by different techniques and exposed to the atmosphere. In situ determined optical constants will allow a more accurate design of multilayers containing ion-beam-sputtered SiC layers.

Intrinsic colorization

In this paper, we present an example-based colorization technique robust to illumination differences between grayscale target and color reference images. To achieve this goal, our method performs color transfer in an illumination-independent domain that is relatively free of shadows and highlights. It first recovers an illumination-independent intrinsic reflectance image of the target scene from multiple color references obtained by web search. The reference images from the web search may be taken from different vantage points, under different illumination conditions, and with different cameras. Grayscale versions of these reference images are then used in decomposing the grayscale target image into its intrinsic reflectance and illumination components. We transfer color from the color reflectance image to the grayscale reflectance image, and obtain the final result by relighting with the illumination component of the target image. We demonstrate via several examples that our method generates results with excellent color consistency.