Incident Radiance Research Articles

Further study on carbon fixation using green power for a solar-assisted multi-generation system with carbon capture

For carbon-based thermal systems, a serious challenge to achieving carbon neutralization is carbon emission reduction. Currently, advanced chemical looping combustion can help thermal systems to realize carbon capture without energy consumption. This study further develops the carbon fixation process of thermal systems using green power. In detail, hydrogen generation and methanol synthesis units are integrated into a multi-generation system, which follows the principle of energy quality matching. Green power, solar heat, methane, high- and low-temperature heat, and mechanical work are reasonably utilized in hydrogen generation, preheating reactants, methanol synthesis, and purifying methanol. The case study shows that the proposed system’s exergy efficiency is enhanced by 10 % compared with the reference system. In addition, about 43 % of carbon dioxide is stored in the form of methanol without cutting down the performance of system components. The energy and exergy efficiencies in the methanol synthesis are 56.36 % and 75.66 %, respectively, indicating the advantages of the proposed system over those proposed in extant research. In addition, these efficiencies are still higher than 50 % and 75 %, respectively, when the incident radiance is larger than 600 W/m2. This study also investigates the effect of electrolytic cell stack and incident irradiance on system performance. Results indicate that the proposed system can directly utilize fluctuating green power without using expensive batteries; moreover, the irreversibility of the multi-generation system is improved. These findings not only broaden the scope of application of green power but also allow to further realize the clean and efficient utilization of fossil fuels.

CALIBRATION OF A LIGHT HEMISPHERICAL RADIANCE FIELD IMAGING SYSTEM

Abstract. A light hemispherical radiance field imaging system based on fish-eye camera was developed for the measurement of the surface incident radiance in an urban environment, which is often affected by radiometric heterogeneity problems. A linear radiometric model and a polynomial fish-eye lens model are used. A temperature-dependent dark level model is proposed to improve the dark correction for high dynamic range photography. This paper describes the calibration procedure for spectral and geometrical radiance field measurements and presents the results of the calibration. The spectral radiometric calibration error is 2.07%, 1.34%, and 0.98% for blue, green, and red bands, respectively. The mean geometrical calibration error is 2.037 pixels.

Gaussian Process for Radiance Functions on the Sphere

AbstractEfficient approximation of incident radiance functions from a set of samples is still an open problem in physically based rendering. Indeed, most of the computing power required to synthesize a photo‐realistic image is devoted to collecting samples of the incident radiance function, which are necessary to provide an estimate of the rendering equation solution. Due to the large number of samples required to reach a high‐quality estimate, this process is usually tedious and can take up to several days. In this paper, we focus on the problem of approximation of incident radiance functions on the sphere. To this end, we resort to a Gaussian Process (GP), a highly flexible function modelling tool, which has received little attention in rendering. We make an extensive analysis of the application of GPs to incident radiance functions, addressing crucial issues such as robust hyperparameter learning, or selecting the covariance function which better suits incident radiance functions. Our analysis is both theoretical and experimental. Furthermore, it provides a seamless connection between the original spherical domain and the spectral domain, on which we build to derive a method for fast computation and rotation of spherical harmonics coefficients.

Path Guiding Using Spatio‐Directional Mixture Models

AbstractWe propose a learning‐based method for light‐path construction in path tracing algorithms, which iteratively optimizes and samples from what we refer to as spatio‐directional Gaussian mixture models (SDMMs). In particular, we approximate incident radiance as an online‐trained 5D mixture that is accelerated by a D‐tree. Using the same framework, we approximate BSDFs as pre‐trained D mixtures, where is the number of BSDF parameters. Such an approach addresses two major challenges in path‐guiding models. First, the 5D radiance representation naturally captures correlation between the spatial and directional dimensions. Such correlations are present in, for example parallax and caustics. Second, by using a tangent‐space parameterization of Gaussians, our spatio‐directional mixtures can perform approximate product sampling with arbitrarily oriented BSDFs. Existing models are only able to do this by either foregoing anisotropy of the mixture components or by representing the radiance field in local (normal aligned) coordinates, which both make the radiance field more difficult to learn. An additional benefit of the tangent‐space parameterization is that each individual Gaussian is mapped to the solid sphere with low distortion near its centre of mass. Our method performs especially well on scenes with small, localized luminaires that induce high spatio‐directional correlation in the incident radiance.

Product path guiding with semi-adaptive spatio-directional tree

Monte Carlo integration is an established method in simulating light transport. However, due to its stochastic nature, it requires copious amounts of samples to eliminate the estimation error, and variance reduction techniques such as importance sampling are used to improve the convergence speed as a remedy. Path guiding is a class of adaptive importance sampling methods devised specifically for light transport simulation, which demonstrates significant improvements over classical sampling techniques. Yet most of the previous path guiding methods only account for the radiance term, omitting the bidirectional scattering distribution function (BSDF) term. This results in suboptimal sample quality for non-diffuse light transport. This paper presents a path guiding method which guides paths according to the full product of the BSDF and radiance terms in light transport equation. Extending a spatio-directional tree based path guiding method, we use a semi-adaptive grid-quadtree hybrid subdividing the directional domain in a spatial subdomain to learn and represent the radiance field. With the help of BSDF lookup tables used to accelerate BSDF evaluations, this grid-quadtree allows us to efficiently construct approximate product distributions and guide paths according to the product of incident radiance and cosine-weighted BSDF at each path vertex. The resulting method is relatively simple to implement and enables more robust sampling on scenes with many glossy surfaces.

Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards.

With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pixel number (DN) and incident radiance and applies a grey reference card to derive water leaving reflectance. However, image DNs change with incident light brightness non-linearly, according to a power function. We developed an improved method for observing and calculating water leaving reflectance from digital images based on multiple reflectance reference cards. The method was applied to acquire water, sky, and reflectance reference card images using a Cannon 50D digital camera at 31 sampling stations; the results were validated using synchronously measured water leaving reflectance using a field spectrometer. The R2 for the red, green, and blue color bands were 0.94, 0.95, 0.94, and the mean relative errors were 27.6%, 29.8%, 31.8%, respectively. The validation results confirm that this method can derive accurate water leaving reflectance, especially when compared with the results derived by HydroColor, which systematically overestimates water leaving reflectance. Our results provide a more accurate theoretical foundation for quantitative water quality monitoring using digital and smartphone cameras.

Robust fitting of parallax-aware mixtures for path guiding

Effective local light transport guiding demands for high quality guiding information, i.e., a precise representation of the directional incident radiance distribution at every point inside the scene. We introduce a parallax-aware distribution model based on parametric mixtures. By parallax-aware warping of the distribution, the local approximation of the 5D radiance field remains valid and precise across large spatial regions, even for close-by contributors. Our robust optimization scheme fits parametric mixtures to radiance samples collected in previous rendering passes. Robustness is achieved by splitting and merging of components refining the mixture. These splitting and merging decisions minimize and bound the expected variance of the local radiance estimator. In addition, we extend the fitting scheme to a robust, iterative update method, which allows for incremental training of our model using smaller sample batches. This results in more frequent training updates and, at the same time, significantly reduces the required sample memory footprint. The parametric representation of our model allows for the application of advanced importance sampling methods such as radiance-based, cosine-aware, and even product importance sampling. Our method further smoothly integrates next-event estimation (NEE) into path guiding, avoiding importance sampling of contributions better covered by NEE. The proposed robust fitting and update scheme, in combination with the parallax-aware representation, results in faster learning and lower variance compared to state-of-the-art path guiding approaches.

Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning

Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient incident radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce incident radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the incident radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irradiance caching.

Offline Deep Importance Sampling for Monte Carlo Path Tracing

AbstractAlthough modern path tracers are successfully being applied to many rendering applications, there is considerable interest to push them towards ever‐decreasing sampling rates. As the sampling rate is substantially reduced, however, even Monte Carlo (MC) denoisers–which have been very successful at removing large amounts of noise–typically do not produce acceptable final results. As an orthogonal approach to this, we believe that good importance sampling of paths is critical for producing better‐converged, path‐traced images at low sample counts that can then, for example, be more effectively denoised. However, most recent importance‐sampling techniques for guiding path tracing (an area known as “path guiding”) involve expensive online (per‐scene) training and offer benefits only at high sample counts. In this paper, we propose an offline, scene‐independent deep‐learning approach that can importance sample first‐bounce light paths for general scenes without the need of the costly online training, and can start guiding path sampling with as little as 1 sample per pixel. Instead of learning to “overfit” to the sampling distribution of a specific scene like most previous work, our data‐driven approach is trained a priori on a set of training scenes on how to use a local neighborhood of samples with additional feature information to reconstruct the full incident radiance at a point in the scene, which enables first‐bounce importance sampling for new test scenes. Our solution is easy to integrate into existing rendering pipelines without the need for retraining, as we demonstrate by incorporating it into both the Blender/Cycles and Mitsuba path tracers. Finally, we show how our offline, deep importance sampler (ODIS) increases convergence at low sample counts and improves the results of an off‐the‐shelf denoiser relative to other state‐of‐the‐art sampling techniques.

Combined pre-treatments effects on zucchini (Cucurbita pepo L.) squash microbial load reduction

Freezing vegetables requires pre-treatments to reduce microbial load and destroy enzymes that impair the frozen product quality. So far blanching has been the most effective pre-treatment, preferred by the food industry, despite its severity: heating up to temperatures close to 100 °C for 1–3 min causes sensory and texture changes in most horticultural products. Alternative blanching treatments, using UV-C radiation combined with milder thermal treatments or with thermosonication, may improve the quality of the final frozen vegetables. Zucchini (Cucurbita pepo L.), the vegetable under study, has an availability in fresh restricted to a season, needing therefore to be often frozen to be used throughout the year. In this study, its surface was first inoculated with two vegetable contaminants, Enterococcus faecalis and Deinococcus radiodurans cells, which are resistant, respectively, to high temperatures and to radiation and then submitted to several blanching treatments, single or combined, and the effect on these microorganisms reduction was evaluated. As single treatments, water blanching (the control treatment, as it is the blanching treatment traditionally used) was applied up to 180 s at temperatures ranging from 65 to 90 °C, and UV-irradiation applied in continuous. As combined pre-treatments, water blanching combined with UV-C (continuous or in pulses), and thermosonication (20 kHz at 50% of power) combined with UV-C pulses were also studied. The continuous UV-C radiation incident irradiance was 11 W/m2 up to 180 s, and the pulses at incident radiance of 67 W/m2, lasting 3.5 s each (35 pulses). Mathematical modeling of bacterial reduction data was carried out using the Bigelow, the Weibull and Weibull modified models, and estimation of their respective kinetic parameters proved that the latter models presented a better fit below 75 °C. The best results proved to be the combination of water blanching at temperatures as low as 85 °C during <2 min with 25 pulses of UV-C (incident irradiance of 67 W/m2) or thermosonication at 90 °C also combined with UV-C pulses, both resulting in 3 log reductions of both microorganisms under study. These results proved to overcome what industry is requiring so far (a 2 log microbial reduction in 3 min), hence minimizing quality changes of frozen zucchini.

Volume Path Guiding Based on Zero-Variance Random Walk Theory

The efficiency of Monte Carlo methods, commonly used to render participating media, is directly linked to the manner in which random sampling decisions are made during path construction. Notably, path construction is influenced by scattering direction and distance sampling, Russian roulette, and splitting strategies. We present a consistent suite of volumetric path construction techniques where all these sampling decisions are guided by a cached estimate of the adjoint transport solution . The proposed strategy is based on the theory of zero-variance path sampling schemes, accounting for the spatial and directional variation in volumetric transport. Our key technical contribution, enabling the use of this approach in the context of volume light transport, is a novel guiding strategy for sampling the particle collision distance proportionally to the product of transmittance and the adjoint transport solution (e.g., in-scattered radiance). Furthermore, scattering directions are likewise sampled according to the product of the phase function and the incident radiance estimate. Combined with guided Russian roulette and splitting strategies tailored to volumes, we demonstrate about an order-of-magnitude error reduction compared to standard unidirectional methods. Consequently, our approach can render scenes otherwise intractable for such methods, while still retaining their simplicity (compared to, e.g., bidirectional methods).

Quadratic detection of an enhanced self-mixing laser Doppler signal with two-photon absorption

A new detection scheme is proposed for an enhanced self-mixing laser Doppler velocimeter (SLDV) using two-photon absorption (TPA), which is based on the TPA-induced quadratic photoconductivity dependence of a photodiode on the incident radiance at 1550 nm with self-mixing interference (SMI). The harmonics of the SMI signal in the frequency spectrum are enhanced simultaneously, as for a fiber-Bragg-grating-based enhanced SLDV. The quadratic response induced by TPA can suppress the undesirable harmonics, making their amplitude into the same level as the noise. From the experimental results presented in this paper, the TPA effect in a commercial Si photodiode implements a sort of ultra-wide bandwidth filter without passively adjusting the cutoff frequencies for an unknown Doppler frequency, which simplifies Doppler signal processing. The signal-to-noise ratio (SNR) is also improved to a considerable level of 15 dB for a Doppler frequency of 4 kHz due to the amplification of an erbium-doped fiber amplifier. This paper provides guidance for the design of the enhanced SLDV with optical filtration.

Multiple Scattering in Inhomogeneous Participating Media Using Rao‐Blackwellization and Control Variates

AbstractRendering inhomogeneous participating media requires a lot of volume samples since the extinction coefficient needs to be integrated along light paths. Ray marching makes small steps, which is time consuming and leads to biased algorithms. Woodcocklike approaches use analytic sampling and a random rejection scheme guaranteeing that the expectations will be the same as in the original model. These models and the application of control variates for the extinction have been successful to compute transmittance and single scattering but were not fully exploited in multiple scattering simulation. Our paper attacks the multiple scattering problem in heterogeneous media and modifies the light–medium interaction model to allow the use of simple analytic formulae while preserving the correct expected values. The model transformation reduces the variance of the estimates with the help of Rao‐Blackwellization and control variates applied both for the extinction coefficient and the incident radiance. Based on the transformed model, efficient Monte Carlo rendering algorithms are obtained.

Real-time global illumination by precomputed local reconstruction from sparse radiance probes

We present a direct-to-indirect transport technique that enables accurate real-time rendering of indirect illumination in mostly static scenes of complexity on par with modern games while supporting fully dynamic lights, cameras and diffuse surface materials. Our key contribution is an algorithm for reconstructing the incident radiance field from a sparse set of local samples --- radiance probes --- by incorporating mutual visibility into the reconstruction filter. To compute global illumination, we factorize the direct-to-indirect transport operator into global and local parts, sample the global transport with sparse radiance probes at real-time, and use the sampled radiance field as input to our precomputed local reconstruction operator to obtain indirect radiance. In contrast to previous methods aiming to encode the global direct-to-indirect transport operator, our precomputed data is local in the sense that it needs no long-range interactions between probes and receivers, and every receiver depends only on a small, constant number of nearby radiance probes, aiding compression, storage, and iterative workflows. While not as accurate, we demonstrate that our method can also be used for rendering indirect illumination on glossy surfaces, and approximating global illumination in scenes with large-scale dynamic geometry.

Image quality and spectral performance evaluations of a polarization imaging spectrometer based on a Savart polariscope.

The modulation transfer function (MTF) and signal-to-noise ratio (SNR) are the key parameters to evaluate quantitatively the image quality and spectral performance in a polarization imaging spectrometer based on a Savart polariscope. In order to evaluate the image quality and reflect the detecting ability of the imaging spectrometer, calibration experiments on the MTF, SNR, and spectral resolution were carried out and some important conclusions were obtained. For incident radiance values 4.464, 3.119, and 0.523 w/m2·sr, the average SNRs of the interferogram were 500, 400, and 200 dB, respectively, and the MTF is 0.24. During the spectral resolution calibration, the maximum optical path difference was set as 57.08 µm, and the measured value is greater than the theoretical value, which is mainly caused by the structural design of the polarization imaging spectrometer. For the wavelength range of [500 nm, 600 nm], the SNR of the spectrum is lower and about 50 dB, while the SNR is obviously higher in a range of λ∈[600 nm, 960 nm]. This study provides a theoretical and practical guidance for improving the image quality and judging the spectral performance of the polarization imaging spectrometer.

Practical Path Guiding for Efficient Light‐Transport Simulation

AbstractWe present a robust, unbiased technique for intelligent light‐path construction in path‐tracing algorithms. Inspired by existing path‐guiding algorithms, our method learns an approximate representation of the scene's spatio‐directional radiance field in an unbiased and iterative manner. To that end, we propose an adaptive spatio‐directional hybrid data structure, referred to as SD‐tree, for storing and sampling incident radiance. The SD‐tree consists of an upper part—a binary tree that partitions the 3D spatial domain of the light field—and a lower part—a quadtree that partitions the 2D directional domain. We further present a principled way to automatically budget training and rendering computations to minimize the variance of the final image. Our method does not require tuning hyperparameters, although we allow limiting the memory footprint of the SD‐tree. The aforementioned properties, its ease of implementation, and its stable performance make our method compatible with production environments. We demonstrate the merits of our method on scenes with difficult visibility, detailed geometry, and complex specular‐glossy light transport, achieving better performance than previous state‐of‐the‐art algorithms.

Interactive directional subsurface scattering and transport of emergent light

Existing techniques for interactive rendering of deformable translucent objects can accurately compute diffuse but not directional subsurface scattering effects. It is currently a common practice to gain efficiency by storing maps of transmitted irradiance. This is, however, not efficient if we need to store elements of irradiance from specific directions. To include changes in subsurface scattering due to changes in the direction of the incident light, we instead sample incident radiance and store scattered radiosity. This enables us to accommodate not only the common distance-based analytical models for subsurface scattering but also directional models. In addition, our method enables easy extraction of virtual point lights for transporting emergent light to the rest of the scene. Our method requires neither preprocessing nor texture parameterization of the translucent objects. To build our maps of scattered radiosity, we progressively render the model from different directions using an importance sampling pattern based on the optical properties of the material. We obtain interactive frame rates, our subsurface scattering results are close to ground truth, and our technique is the first to include interactive transport of emergent light from deformable translucent objects.

Radiance and photon noise: imaging in geometrical optics, physical optics, quantum optics and radiology.

The statistics of detector outputs produced by an imaging system are derived from basic radiometric concepts and definitions. We show that a fundamental way of describing a photon-limited imaging system is in terms of a Poisson random process in spatial, angular, and wavelength variables. We begin the paper by recalling the concept of radiance in geometrical optics, radiology, physical optics, and quantum optics. The propagation and conservation laws for radiance in each of these domains are reviewed. Building upon these concepts, we distinguish four categories of imaging detectors that all respond in some way to the incident radiance, including the new category of photon-processing detectors (capable of measuring radiance on a photon-by-photon basis). This allows us to rigorously show how the concept of radiance is related to the statistical properties of detector outputs and to the information content of a single detected photon. A Monte-Carlo technique, which is derived from the Boltzmann transport equation, is presented as a way to estimate probability density functions to be used in reconstruction from photon-processing data.

Black Sun: Ocular Invisibility of Relativistic Luminous Astrophysical Bodies

The relativistic Doppler shifting of visible electromagnetic radiation to beyond the human ocular range reduces the incident radiance of the source. Consequently, luminous astrophysical bodies (LABs) can be rendered invisible with sufficient relativistic motion. This paper determines the proper distance as a function of relativistic velocity at which a luminous object attains ocular invisibility.