Illumination Model Research Articles

Toward generalizable phenotype prediction from single-cell morphology representations

BackgroundFunctional cell processes (e.g., molecular signaling, response to stimuli, mitosis, etc.) impact cell phenotypes, which scientists can measure with cell morphology. However, linking these measurements with phenotypes remains challenging because it requires manually annotated labels. We propose that nuclear morphology can be a predictive marker for cell phenotypes that are generalizable across contexts.MethodsWe reanalyzed a pre-labeled, publicly-available nucleus microscopy dataset from the MitoCheck consortium. We extracted single-cell morphology features using CellProfiler and DeepProfiler, which provide robust processing pipelines. We trained multinomial, multi-class elastic-net logistic regression models to classify nuclei into one of 15 phenotypes such as ‘Anaphase,’ ‘Apoptosis’, and ‘Binuclear’. We rigorously assessed performance using F1 scores, precision-recall curves, and a leave-one-image-out (LOIO) cross-validation analysis. In LOIO, we retrained models using cells from every image except one and predicted phenotype in the held-out image, repeating this procedure for all images. We evaluated each morphology feature space, a concatenated feature space, and several feature space subsets (e.g., nuclei AreaShape features only). We applied models to the Joint Undertaking in Morphological Profiling (JUMP) data to assess performance using a different dataset.ResultsIn a held-out test set, we observed an overall F1 score of 0.84. Individual phenotype scores ranged from 0.64 (moderate performance) to 0.99 (high performance). Phenotypes such as ‘Elongated’, ‘Metaphase’, and ‘Apoptosis’ showed high performance. While CellProfiler and DeepProfiler features were generally equally effective, concatenation yielded the best results for 9/15 phenotypes. LOIO showed a performance decline, indicating our model could not reliably predict phenotypes in new images. Poor performance was unrelated to illumination correction or model selection. Applied to the JUMP data, models trained using nuclear AreaShape features only increased alignment with the annotated MitoCheck data (based on UMAP space). This approach implicated many chemical and genetic perturbations known to be associated with specific phenotypes.DiscussionPoor LOIO performance demonstrates challenges of single-cell phenotype prediction in new datasets. We propose several strategies that could pave the way for more generalizable methods in single-cell phenotype prediction, which is a step toward morphology representation ontologies that would aid in cross-dataset interpretability.

PSNet: A non-uniform illumination correction method for underwater images based pseudo-siamese network

Autonomous underwater vehicles (AUVs) based on visual perception play an important role in maritime operations. However, underwater environment often suffers from poor lighting conditions, making the utilization of artificial light sources necessary. This reliance on artificial lighting frequently results in non-uniform illumination. Furthermore, the absorption and scattering effects of water cause further degradation, such as color distortion and blurring of details. To address these challenges, we propose a pseudo-siamese network, named PSNet, designed for underwater optical image enhancement. PSNet separates the non-uniformly illuminated layer from the optimally uniformly illuminated image and utilizes a cascading iteration strategy to enhance the image details. To achieve a better balance prediction quality, we introduce structure loss and residual reconstruction loss as additional guides for model learning. Additionally, we incorporate a color consistency loss to mitigate color distortion. To address the lack of training data, we develop a non-uniform illumination model and generate a dataset that includes both non-uniformly illuminated layers and uniformly illuminated images. Through comprehensive experimental evaluations, PSNet significantly enhances the visual quality of underwater optical images and consistently outperforms state-of-the-art approaches in multiple performance metrics.

A multi-timescale image space model for dynamic cloud illumination

Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene illumination. While there have been recent advances in developing sky models which include clouds, these all neglect cloud movement which is a crucial component of cloudy sky appearance. In any sort of video or interactive environment, it can be expected that clouds will move, sometimes quite substantially in a short period of time. Our work proposes a solution to this which enables whole-sky dynamic cloud synthesis for the first time. We achieve this by proposing a multi-timescale sky appearance model which learns to predict the sky illumination over various timescales, and can be used to add dynamism to previous static, cloudy sky lighting approaches.

Research on Energy Efficiency Optimization Control Strategy of Office Space Based on Genetic Simulated Annealing Strategy

Current energy-saving lighting control algorithms often face the dilemma of local optimality, which limits the energy-saving potential and comfort improvement of indoor lighting systems. The control parameters of the lighting system are optimized using a genetic simulated annealing algorithm to achieve the global optimal solution and enhance energy-saving efficacy in indoor lighting. The local search ability of the algorithm is enhanced by simulated annealing processing of excellent individuals after genetic operation. The genetic probability is adaptively adjusted according to the number of iterations and the fitness of the population, so that the algorithm enriches the population diversity in the early stage and avoids the “premature” convergence of the algorithm. A lamp illuminance model based on an artificial neural network and an indoor natural illuminance model based on a workbench are proposed to evaluate the lighting comfort, which provides a basis for constructing the fitness function of the optimization algorithm. Through the simulation experiment, the genetic simulated annealing algorithm is applied to the lighting scene introduced in this paper and compared with the traditional particle swarm optimization algorithm and genetic algorithm, the lighting energy saving performance is significantly improved.

Shape-from-Shading Derived DEMs from ShadowCam Images in Permanently Shadowed Regions at the Lunar South Pole: A First Look

Abstract. The lunar south pole, particularly its permanently shadowed regions (PSRs), has been the focus of future lunar exploration. The solar altitude angle at the lunar south pole is less than ∼ 2°, resulting in the PSRs not being directly illuminated and the possible existence of water ice in the PSRs due to the low temperatures. High-resolution digital elevation models (DEMs) in PSRs are crucial for future exploration missions and scientific research. However, existing image datasets (e.g., LROC NAC images) cannot penetrate the PSRs. Laser altimetry (e.g., LOLA) can measure the PSRs, but the DEMs derived from LOLA suffer from noises and interpolation defects. ShadowCam is a newly deployed camera onboard the Korea Pathfinder Lunar Orbiter, with a high imaging sensitivity. ShadowCam can capture the faint secondary illumination within PSRs, revealing previously indistinguishable topographic features in persistent darkness. Based on our existing Shape-from-Shading (SfS) pipeline for generating high-resolution DEMs from monocular images, we introduce a novel SfS approach that utilizes secondary illumination and reflectance to reconstruct pixel-wise high-resolution DEMs from ShadowCam images in PSRs. The approach consists of three steps: (1) modeling the secondary illumination on the target surfaces in PSRs based on the view factor; (2) optimizing a low-resolution LOLA DEM using the secondary illumination model; (3) refining the DEM to pixel-wise resolution based on SfS using the ShadowCam images. A test area (approximately 1 km × 1 km) within the PSR in the Shackleton crater near the lunar south pole is used for experimental evaluation. The results show that the DEM generated by the proposed approach is highly consistent with the direct LOLA measurements, particularly in small-scale topographic features such as crater floors and rims that are absent from the LOLA DEM. A first look at the results indicates that the proposed approach has great potential for generating high-resolution DEMs from ShadowCam images in PSRs, which can aid future exploration missions targeting the PSRs at the lunar south pole and support related scientific research on the water ice in the PSRs.

Toward a fast and non-darkroom solution for speckle correlation based scattering imaging

Speckle correlation-based scattering imaging is an ingenious field, as it allows for the efficient reconstruction of object images using computational techniques in a simple setup. However, this method typically necessitates high-contrast speckle images captured in a darkroom environment, restricting its applicability to specific scenarios. Here, we present a fast and non-darkroom imaging framework, namely FNDI, for reconstructing objects through scattering media under ambient light interference. Specifically, a speckle illumination model is established guided by the total variational Retinex (TV-Retinex) theory, and the speckle illumination component is adjusted to obtain an enhanced speckle with significantly improved contrast. Then, a modified Fienup algorithm with the iteration-driven shrinkwrap (IDS) strategy is employed to rapidly reconstruct the object image through tens of iterations. Extensive experiments are conducted under different lighting conditions to evaluate FNDI in comparison with existing non-darkroom methods and the classical speckle correlation method. The results demonstrate that FNDI is effective and efficient, making it highly attractive for practical scattering imaging applications.

Real-time volume rendering for three-dimensional fetal ultrasound using volumetric photon mapping

Three-dimensional (3D) fetal ultrasound has been widely used in prenatal examinations. Realistic and real-time volumetric ultrasound volume rendering can enhance the effectiveness of diagnoses and assist obstetricians and pregnant mothers in communicating. However, this remains a challenging task because (1) there is a large amount of speckle noise in ultrasound images and (2) ultrasound images usually have low contrasts, making it difficult to distinguish different tissues and organs. However, traditional local-illumination-based methods do not achieve satisfactory results. This real-time requirement makes the task increasingly challenging. This study presents a novel real-time volume-rendering method equipped with a global illumination model for 3D fetal ultrasound visualization. This method can render direct illumination and indirect illumination separately by calculating single scattering and multiple scattering radiances, respectively. The indirect illumination effect was simulated using volumetric photon mapping. Calculating each photon’s brightness is proposed using a novel screen-space destiny estimation to avoid complicated storage structures and accelerate computation. This study proposes a high dynamic range approach to address the issue of fetal skin with a dynamic range exceeding that of the display device. Experiments show that our technology, compared to conventional methodologies, can generate realistic rendering results with far more depth information.

Precomputed low-frequency lighting in cinematic volume rendering.

Cinematic Rendering (CR) employs physical models such as ray tracing and global illumination to simulate real-world light phenomena, producing high-quality images with rich details. In the medical field, CR can significantly aid doctors in accurate diagnosis and preoperative planning. However, doctors require efficient real-time rendering when using CR, which presents a challenge due to the substantial computing resources demanded by CR's ray tracing and global illumination models. Precomputed lighting can enhance the efficiency of real-time rendering by freezing certain scene variables. Typically, precomputed methods freeze geometry and materials. However, since the physical rendering of medical images relies on volume data rendering of transfer functions, the CR algorithm cannot utilize precomputed methods directly. To improve the rendering efficiency of the CR algorithm, we propose a precomputed low-frequency lighting method. By simulating the lighting pattern of shadowless surgical lamps, we adopt a spherical distribution of multiple light sources, with each source capable of illuminating the entire volume of data. Under the influence of these large-area multi-light sources, the precomputed lighting adheres to physical principles, resulting in shadow-free and uniformly distributed illumination. We integrated this precomputed method into the ray-casting algorithm, creating an accelerated CR algorithm that achieves more than twice the rendering efficiency of traditional CR rendering.

The thermal impact of the self-heating effect on airless bodies. The case of Mercury’s north polar craters

Thermal models are essential for studying airless planetary surfaces, as the interaction between topography and thermophysical properties plays a crucial role in determining a surface’s response to localized illumination. Accurate temperature distribution calculations require a comprehensive investigation of sunlight scattering, a process that, despite its computational challenges, cannot be overlooked, especially when high resolution is necessary. Furthermore, thermal analysis is fundamental for assessing the stability of volatiles in polar regions. In this study, we introduce a novel approach by discretizing the Sun into 100 individual elements, allowing for a highly precise simulation of solar flux—an innovation crucial for accurately capturing temperature distributions in Mercury’s polar craters, given the planet’s proximity to the Sun. This level of discretization significantly enhances the accuracy of the thermal model, ensuring a more realistic depiction of how sunlight interacts with crater topography. We developed a dual-model approach that simulates both direct solar illumination and its scattering on two craters, Laxness and Fuller, located at Mercury’s north pole. The illumination and thermal model predict temperature distribution and heat transfer based on the material’s thermal properties and topography. The study examines the interaction between direct sunlight, causing localized heating, and scattered light, which influences the thermal response of surface materials. Detailed illumination maps and temperature profiles were generated over two Hermean years, revealing the significant impact of the self-heating effect on temperature distribution. The results show that specific regions experience indirect solar flux due to the craters’ morphology, particularly in permanently shadowed regions (PSRs) that are heated exclusively by scattered radiation. Maximum temperature profiles for the Laxness and Fuller craters show a substantial temperature increase within PSRs compared to areas exposed to direct illumination. However, while self-heating does not affect the stability of water ice in the Laxness crater, in the Fuller crater, a section within the radar-bright material reaches temperatures of up to 210 K, potentially threatening the stability of water ice. Further investigation with the onboard SIMBIO-SYS instrument on the BepiColombo mission will help to better understand the current state of these craters and their volatile deposits.

EVALUASI PEMBELAJARAN TEMATIK DALAM MEMBENTUK PROFIL PELAJAR PANCASILA PADA MADRASAH IBTIDAIYAH DI KABUPATEN SINTANG

The aim of this study is to describe thematic learning in forming the Pancasila Student Profile by knowing the planning, implementation and knowing the learning assessment tools in forming the Pancasila Student Profile. This study uses an illuminative evaluation model, with data sources coming from four Elementary Madrasah in Sintang Regency. The data were qualitative data collected through observation, interview, and documentation techniques. Triangulation of techniques and sources is used to ensure the validity of the data. The results of the analysis of learning carried out at class IV in four (4) Elementary Madrasah in Sintang Regency in forming the Pancasila Student Profile found 1) Each Madrasah has a learning planning formulation consisting of a syllabus document and a Learning Implementation Plan. The syllabus document adopts the formulation issued by the Education and Culture Office or downloaded via the internet; 2) The learning implementation process at Madrasah Ibtidaiyah in Sintang Regency is carried out based on the characteristics of each Madrasah while still adhering to the syllabus. Some Madrasah use Dhuha prayer as an opening activity, some use Tahfidz activities as routine activities in the morning. The implementation of the three stages in learning in general is carried out well; 3). The critical reasoning aspect according to Bloom's taxonomy theory has not been reflected in the learning assessment tools which are prepared or used by teachers.

SOHO SWAN Lyα Models Supporting LRO LAMP: 2008–2023

The Lunar Reconnaissance Orbiter Lyman-Alpha Mapping Project (LAMP) has been mapping the Moon since its launch in 2009. Faint ultraviolet illumination of the lunar dark side includes light from stars and from hydrogen Lyα emissions, mostly attributed to sunlight scattered by hydrogen atoms near the Sun with a smaller contribution from the whole Galaxy. Models of the lunar illumination by time-dependent Lyα photons have allowed the LAMP team to map polar shadowed craters suspected of harboring water ice and other volatiles. This paper describes the model that provides daily all-sky Lyα maps tuned by comparisons with all-sky Lyα maps from the SOlar and Heliospheric Observatory Solar Wind ANisotropy Experiment stationed at the Sun–Earth L1 point.

Comparative analysis of circadian lighting models: melanopic illuminance vs. circadian stimulus.

The influence of light exposure on human circadian rhythms has been widely recognized. This effect is mediated by a phototransduction process projected by the intrinsically photosensitive retinal ganglion cells (ipRGCs). The process also involves signal inputs from visual photoreceptors. However, the relative contributions of each photoreceptor to this process remain unclear; accordingly, two different types of circadian lighting models have been proposed: (i) the melanopic illuminance model based solely on ipRGC activation, including melanopic equivalent daylight D65 illuminance (m-EDI) and equivalent melanopic illuminance (EML), and (ii) the circadian stimulus (CS) model, which considers the participation of both ipRGC and visual photoreceptors. However, the two models can yield conflicting predictions. In this study, we assessed and compared the accuracies of these circadian lighting models by fitting a substantial amount of experimental data extracted from multiple laboratory studies. Upon evaluating the results across all exposure durations, data-fitting accuracy of the intricate CS model did not surpass that of the much simpler melanopic illuminance model. Consequently, the latter appears to be the more suitable model for lighting applications. Moreover, a recurring limitation of prior research was revealed: the lighting spectra were not tailored to effectively reflect the fundamental distinctions between the two types of models. Therefore, drawing clear conclusions regarding the accuracies of the models is challenging. To address this problem, we introduced a method for designing contrast-spectra pairs. This method can provide lighting spectra to highlight the difference in circadian illuminance based on one model while keeping the circadian illuminance of others constant.

A dual domain multi-exposure image fusion network based on spatial-frequency integration

Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. Existing deep learning-based multi-exposure image fusion methods primarily focus on spatial domain fusion, neglecting the global modeling ability of the frequency domain. To effectively leverage the global illumination modeling ability of the frequency domain, we propose a novelty perspective on multi-exposure image fusion via the Spatial-Frequency Integration Framework, named MEF-SFI. Initially, we revisit the properties of the Fourier transform on the 2D image, and verify the feasibility of multi-exposure image fusion in the frequency domain where the amplitude and phase component is able to guide the integration of the illumination information. Subsequently, we present the deep Fourier-based multi-exposure image fusion framework, which consists of a spatial path and frequency path for local and global modeling separately. Specifically, we introduce a Spatial-Frequency Fusion Module to facilitate efficient interaction between dual domains and capture complementary information from input images with different exposures. Finally, we combine a dual domain loss function to ensure the retention of complementary information in both the spatial and frequency domains. Extensive experiments on the PQA-MEF dataset demonstrate that our method achieves visual-appealing fusion results against state-of-the-art multi-exposure image fusion approaches. Our code is available at https://github.com/SSyangguang/MEF-freq.

Research on spatial carving method of glutenite reservoir based on opacity voxel imaging

The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.

Evaluation of the Implementation of the International Credit Transfer Program of Makassar State University

In 2021, Universitas Negeri Makassar (UNM) first became one of the universities participating in the International Credit Transfer Program (ICT) and made Universiti Kebangsaan Malaysia (UKM) one of the partner universities. Thus, this study aims to evaluate the implementation of ICT held online by UNM for one semester. The evaluation model used in this study is an illuminative evaluation model that focuses on qualitative data collection using observation, interview, and document review methods. The results showed that the implementation of ICT by UNM went quite well. This is evidenced by the achievement of the goals of the ICT program for universities and students. In addition, this program motivates students to take part in other international programs participated in by UNM and organized by the government. However, there are some problems encountered by UNM and students during the program. Such as communication problems of the committee and participants, to administrative problems. In conclusion, ICT is incredibly good to be followed again by UNM, seeing the magnitude of the impact and enthusiasm of the students. However, improvements to the administrative and communication systems must be completed first before returning to participate in the following years.

Lunar North Polar Cold Traps Based on Diurnally and Seasonally Varying Temperatures

Lunar cold traps are defined by extremely low sublimation rates, such that water ice could have accumulated in them. Here time-averaged sublimation rates are calculated for the north polar region of the Moon based on over 14 years of Diviner surface temperature measurements. Data for each spatial pixel are binned according to subsolar (diurnal) and ecliptic (seasonal) longitude. The cold trap area poleward of 80°N is about 32% larger when defined by a time-average sublimation rate instead of by peak temperature. Apparently sunlit cold traps are identified, e.g., in Lenard Crater, where modeling of direct illumination reveals that the Sun briefly rises above the horizon each Draconic year. The true cold trap area is smaller than what can be determined from Diviner data. Also presented are north polar maps for the potential sublimation rate of relic buried ice and for subsurface cold trapping.

Estimating intrinsic characteristics of images for shadow removal

Existing works show that shadow removal tasks can benefit from the physical illumination model on the formation of shadows. Inspired by prior works that recover the intrinsic characteristics by decomposing an image for its reflectance and illumination components, we study a variant of shadow illumination model that can better reflect the complexity in the real world — in this model, shadow-free pixels can be expressed by a translation formed of reflectance and illumination components. Based on the new illumination model, we develop a new LR-ShadowNet, which contains two sub-nets for estimating the illumination and reflectance components, respectively, and one sub-net for refining the shadow-removal result. Besides, several mask guidance module are incorporated into LR-ShadowNet for guiding components estimation and result refinement based on shadow region information. The whole network is trained in an end-to-end fashion guided by the shadow masks. Extensive experiments on the ISTD dataset and SBU-Timelapse dataset show that the proposed LR-ShadowNet achieves competitive performance with less computational cost and strong generalisation ability.

Shadows and photon rings of a quantum black hole

Recently, a black hole model in loop quantum gravity has been proposed by Lewandowski, Ma, Yang and Zhang (2023) [34]. The metric tensor of the quantum black hole (QBH) is a suitably modified Schwarzschild one. In this paper, we calculate the radius of the circular null geodesic (light ring) and obtain the linear approximation of it with respect to the quantum correction parameter α: rl≃3M−α9M. We then assume the QBH is backlit by a large, distant plane of uniform, isotropic emission and calculate the radius of the black hole shadow and its linear approximation: rs=33M−α6(3M). We also consider the photon ring structures in the shadow when the impact parameter b of the photon approaches to a critical impact parameter bc, and obtain a formula for estimating the deflection angle, which is φdef=−2ωrl2log⁡(1−bc/b)+C˜(bc). We also numerically plot the images of shadows and photon rings of the QBH in three different illumination models and compare them with that of a Schwarzschild black hole. It is found that we could distinguish the quantum black hole with a Schwarzschild black hole via the shadow images in certain illumination models.

Synthetic Document Images with Diverse Shadows for Deep Shadow Removal Networks.

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance.

Visualization-Driven Illumination for Density Plots.

We present a novel visualization-driven illumination model for density plots, a new technique to enhance density plots by effectively revealing the detailed structures in high- and medium-density regions and outliers in low-density regions, while avoiding artifacts in the density field's colors. When visualizing large and dense discrete point samples, scatterplots and dot density maps often suffer from overplotting, and density plots are commonly employed to provide aggregated views while revealing underlying structures. Yet, in such density plots, existing illumination models may produce color distortion and hide details in low-density regions, making it challenging to look up density values, compare them, and find outliers. The key novelty in this work includes (i) a visualization-driven illumination model that inherently supports density-plot-specific analysis tasks and (ii) a new image composition technique to reduce the interference between the image shading and the color-encoded density values. To demonstrate the effectiveness of our technique, we conducted a quantitative study, an empirical evaluation of our technique in a controlled study, and two case studies, exploring twelve datasets with up to two million data point samples.