Illumination Distribution Research Articles

Nighttime visible and infrared image fusion based on adversarial learning

The task of infrared–visible image fusion (IVIF) aims to integrate multi-modal complementary information and facilitate other downstream tasks, especially under some harsh circumstances. To tackle the challenges of preserving significant information and enhancing visual effects under nighttime conditions, we propose a novel IVIF method based on adversarial learning, namely AdvFusion. It consists of an autoencoder-based generator and a dual discriminator. In particular, the multi-scale features of source images are firstly extracted by ResNet, and then aggregated based on the attention mechanisms and nest connection strategy to generate the fused images. Meanwhile, a global and local dual discriminator structure is designed to minimize the distance between the illumination distributions of the reference images and fused images, which achieves contrast enhancement within fused images and helps to uncover hidden cues in darkness. Moreover, a color loss is utilized to maintain color balance of each fused image, while the widely used perceptual loss and gradient loss are employed to maintain content consistency between the source and fused images. Extensive experiments conducted on five datasets demonstrate that our AdvFusion can achieve promising results compared with the state-of-the-art IVIF methods in terms of both visual effects and quantitative metrics. Furthermore, AdvFusion can also boost the performance of semantic segmentation on MSRS dataset and object detection on M3FD dataset.

The Optimization of Transparent Domes With Bionic Architecture Approach and Emphasizing on Providing Optimal Daylighting Using Crab Eye Structures

Statement of the problem. The hypothesis of this research is that it is possible to use the eye structure of aquatic animals, which performs well in low light conditions, to improve, optimize and introduce new daylighting systems. The intended transparent dome in this study is a double glass skin dome with placed modules between the two layers of dome to control daylighting. According to the natural structure investigated in this research, these modules are dynamic and variable in two fully open condition to receive maximum daylight in low dim light conditions (e.g., in the cloudy sky) and semi-closed in the completely sunny sky with intense radiation. Results and conclusions. What is intended as an independent variable in this research are: the opening width, and the shape of modules (square, hexagonal and triangular), the height of the modules and the rotation angle of the light reflecting panels of the modules. The dependent variables (appropriate illuminance and appropriate lighting distribution pattern) are investigated by Useful Daylight Illuminance (UDI). Radiance software have been used as daylight simulators. The results of this research include the optimal physical characteristics of the modules and the performance of variables on the illumination under the dome.

Investigation of the Effect of Parametric Patterned Façade and Resulting Daylight Illuminance on Students’ Mood and Task Performance in Architecture Studio-Based Tutoring

Parametric design is one of the thriving contemporary architectural treatments that not only has an influence on the design of building envelopes but is capable of affecting the users physically and psychologically. The efficiency of the daylight resulting from the facade is greatly affected by the envelope’s perforation shapes, sizes, and distribution. This research builds upon a prior study which investigated the effect of Parametric Patterned Façade on daylight performance, quality, and visual comfort concluding with the choice of the highest-performing pattern for daylight metrics. The chosen pattern is the “Triangles Pattern” which was put in situ where the field experiment took place. The current study analyzed the effect of illuminance, and illuminance distribution resulting from the parametric patterned facade on architecture students’ mood and task performance in the setting of an architecture studio-based tutoring. Both quantitative and qualitative research methods were adopted; quantitative through the utilization of a Spatial Ability Test to assess students’ task performance, and qualitative through a self-reporting mood assessment, and subjective daylight perception questionnaire. The analysis was conducted through three types of tests to investigate the research hypothesis; statistical analysis of hypothesis (t-test), PANAS schedule for mood assessment, and comparative analysis of the subjective questionnaire between the base case condition and the parametric patterned façade condition. The results of paired sample study indicated that the students’ performance was enhanced after the exposure to the second condition (PPF condition) over the first (BC condition). Also, for the NA assessment, both PANAS assessment and t-test analysis results showed a difference in means between the two conditions indicating that there was an enhancement of the NA. Finally, a framework is proposed for the utilization of a Parametric Patterned façade in an educational setting. The implications of this experiment inform architects of the importance of daylighting techniques to enrich the educational environment, and how to establish a research methodology for the enhancement of daylight design using both technical and psychological approaches.

Illumination Correction and Enhancement Algorithm for Defective Powder Spreading Images in Selective Laser Sintering

Abstract Based on the uneven illumination distribution and low defect contrast in the powder spreading images acquired during the selective laser sintering manufacturing process, which affects subsequent defect detection, an adaptive nonlinear illumination correction and enhancement algorithm is proposed. Local illumination correction is performed using gray valueitting curves and is combined with global gamma correction to improve correction capability in overly dark areas. A nonlinear enhancement function is constructed to boost the contrast of the image’s illumination component. Finally, a gradient-based L0 norm minimization algorithm is used to smooth non-defect areas. Experimental results show that the proposed algorithm signi icantly improves correction and enhancement, outperforming comparison algorithms overall. Compared to the original images, the coef icient of variation decreases by an average of 0.179, and the standard deviation increases by an average of 26.7%, demonstrating the effectiveness of the proposed algorithm.

Comparative analysis of hydrogel-based thermotropic glazing and fixed horizontal overhangs in building performance

Thermotropic (TT) glazing is able to achieve energy-saving capabilities similar to external shading devices by dynamically regulating solar heat gain transmitted through windows in response to ambient climatic conditions. However, the building performance of TT glazing compared to traditional shading devices remains underexplored. This study aims to bridge this knowledge gap by comparing the building performance of hydrogel-based TT glazing and fixed horizontal external shading devices in terms of building energy consumption, daylighting performance, and thermal comfort. Experimental tests were conducted over a continuous ten-day period, focusing on the indoor temperature and illumination distribution within experimental rooms equipped with either TT glazing or fixed horizontal external shading device. Building performance models were developed using EnergyPlus and validated with experimental data. Subsequently, a comparative analysis was then conducted to evaluate the impact of TT glazings and fixed horizontal external shading devices on building energy consumption, daylighting performance, and thermal comfort across five typical climate conditions in China. The findings indicated that: (1) In five climate zones, TT provided the greatest energy savings in West-facing orientations, followed by East-facing orientations. (2) TT can replace fixed horizontal external shading device with a certain length to achieve similar building energy efficiency and indoor thermal comfort performance. (3) In regard to daylighting performance, TT achieved the desired values of sUDI<100lx,50 % and sUDI300–3000lx,50 % across all four orientations in the five climates. (4) In terms of thermal comfort, TT improved thermal comfort by reducing the annual daytime average PPDoverheating across all orientations in the five climate zones.

Image harmonization with Simple Hybrid CNN-Transformer Network

Image harmonization seeks to transfer the illumination distribution of the background to that of the foreground within a composite image. Existing methods lack the ability of establishing global–local pixel illumination dependencies between foreground and background of composite images, which is indispensable for sharp and color-consistent harmonized image generation. To overcome this challenge, we design a novel Simple Hybrid CNN-Transformer Network (SHT-Net), which is formulated into an efficient symmetrical hierarchical architecture. It is composed of two newly designed light-weight Transformer blocks. Firstly, the scale-aware gated block is designed to capture multi-scale features through different heads and expand the receptive fields, which facilitates to generate images with fine-grained details. Secondly, we introduce a simple parallel attention block, which integrates the window-based self-attention and gated channel attention in parallel, resulting in simultaneously global–local pixel illumination relationship modeling capability. Besides, we propose an efficient simple feed forward network to filter out less informative features and allow the features to contribute to generating photo-realistic harmonized results passing through. Extensive experiments on image harmonization benchmarks indicate that our method achieve promising quantitative and qualitative results. The code and pre-trained models are available at https://github.com/guanguanboy/SHT-Net.

Multi-objective optimization of switchable suspended particle device vacuum glazing for comfort and energy efficiency in school typologies under hot climate

This study focuses on the multi-objective optimization of a switchable Suspended Particle Device (SPD) vacuum glazing system to reduce energy use while ensuring thermal and visual comfort in schools located in arid desert environments. A detailed synergy analysis was performed on a representative floor from three school classroom typologies, comparing suspended particle device vacuum glazing in its OFF and ON states against conventional double-glazing with air gaps windows. This analysis employed an advanced simulation framework integrating Rhino-Grasshopper with Open Studio Model, Colibri 2.0, and Design Explorer2 software to evaluate energy consumption, Adaptive Comfort Percentage (ACP), and Useful Daylight Illuminance (UDI). Thermal comfort varied across typologies and orientations, with the atrium with skylight typology frequently meeting or approaching the 80 % Adaptive Comfort Percentage benchmark. However, the southern and eastern classrooms in the enclosed atrium with clerestory typology did not exceed a 39 % adaptive comfort percentage. The comprehensive parametric and multi-objective optimization simulation revealed that suspended particle device vacuum glazing in its 'OFF' state did not fulfill the UDI300lux–1000lux criteria across all typologies, even with maximal Window to Wall Ratio (WWR) and skylight ratio (SR). However, the enclosed atrium with clerestory, integrating SPD vacuum glazing with a 40 % Window to Wall Ratio configuration, emerged as particularly effective in achieving optimal useful daylight illuminance distribution, minimizing energy consumption, and ensuring satisfactory adaptive comfort percentage across various orientations. These findings underscore the potential of multi-objective optimization of suspended particle device vacuum glazing, combined with strategic Window to Wall Ratio adjustments, as a viable alternative to traditional glazing systems in hot desert climates. The adaptability of suspended particle device vacuum glazing highlights its significant potential for achieving thermal comfort, enhancing sustainable architectural design, and improving energy efficiency in educational buildings within extreme climate zones.

Design and Application Research of a UAV-Based Road Illuminance Measurement System

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Optimization of lighting design on façades of residential buildings of heritage interest

Lighting in architecture is acquiring special relevance in residential buildings, especially in cities of cultural and heritage interest. The objective of this research was to optimize the lighting design of the residential building named Casa de Doña Jacinta, located in Historic Centre of Córdoba (Spain), included in the World Heritage List by UNESCO. The analysis of three variables was conducted considering the maintenance of its residential activity. The first one, intrusive light minimization through lighting simulation with DIALux validated using a quasi-real model. The second, spatial contrast maximization, via digital metrics, using MATLAB in the rendering images of DIALux. And the third, energy efficiency maximization through average illuminance, power density and utilization factor. In the main module of the building, 302 different luminaire positions were analysed. The resulting algorithm defines the optimal luminaire position and orientation. Considering façade geometric conditions, type of openings and lighting area, this algorithm can be generalized to other buildings. This constitutes an advance in the optimization of these installations towards a more scientific approach that considers variables related to the perception of the illuminated object and, settles a design strategy based on accent lighting and non-uniform distribution of illuminance, greater in solids than in openings.

An Improved Underwater Visual SLAM through Image Enhancement and Sonar Fusion

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee Curve

Images captured under low-light conditions suffer from several combined degradation factors, including low brightness, low contrast, noise, and color bias. Many learning-based techniques attempt to learn the low-to-clear mapping between low-light and normal-light images. However, they often fall short when applied to low-light images taken in wide-contrast scenes because uneven illumination brings illumination-varying noise and the enhanced images are easily over-saturated in highlight areas. In this article, we present a novel two-stage method to tackle the problem of uneven illumination distribution in low-light images. Under the assumption that noise varies with illumination, we design an illumination-aware transformer network for the first stage of image restoration. In this stage, we introduce the Illumination-aware Attention Block featured with Illumination-aware Multi-head Self-attention, which incorporates different scales of illumination features to guide the attention module, thereby enhancing the denoising and reconstruction capabilities of the restoration network. In the second stage, we innovatively introduce a cubic auto-knee curve transfer with a global parameter predictor to alleviate the over-exposure caused by uneven illumination. We also adopt a white balance correction module to address color bias issues at this stage. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively.

Segmentation of THz holograms for homogenous illumination

This paper investigates the feasibility of applying the hologram segmentation method for homogeneous illumination. Research focuses on improving the uniformity of the illumination obtained from diffractive optical elements in the THz range. The structures are designed with a modified Ping-Pong algorithm and a neural network-based solution. This method allows for the improvement of uniform illumination distribution with the desired shape. Additionally, the phase modulations of the structures are divided into segments, each responsible for imaging at different distances. Various segment combination methods are investigated, differing in shapes, image plane distances, and illumination types. The obtained image intensity maps allow for the identification of the performance of each combination method. Each of the presented structures shows significant improvements in the uniformity of imaged targets compared to the reference Ping-Pong structure. The presented structures were designed for a narrow band case—260 GHz frequency, which corresponds to 1.15 mm wavelength. The application of diffractive structures for homogenization of illumination shows promise. The created structures perform designed beamforming task with variability of intensity improved up to 23% (standard deviation) or 45% (interquartile range) compared with reference structure.

A multiscale approach to a linear programming problem for calculating a refractive optical element forming a given far-field illumination distribution

We consider an approach to the calculation of a refractive optical element generating a prescribed irradiance distribution in the far field for a plane incident beam based on a certain variational problem. We consider an explicit formulation of this problem in the form of the Monge-Kantorovich mass transfer problem. We also demonstrate a connection between the mass transfer problem and the dual linear variational problem. A numerical solution of the linear variational problem is also considered. The direct solution of this type of problem presents a huge computational complexity. To overcome this difficulty we use the so-called multiscale approach based on constructing a chain of approximations that are solutions on refining grids.

Temporally Consistent Enhancement of Low-Light Videos via Spatial-Temporal Compatible Learning

Temporal inconsistency is the annoying artifact that has been commonly introduced in low-light video enhancement, but current methods tend to overlook the significance of utilizing both data-centric clues and model-centric design to tackle this problem. In this context, our work makes a comprehensive exploration from the following three aspects. First, to enrich the scene diversity and motion flexibility, we construct a synthetic diverse low/normal-light paired video dataset with a carefully designed low-light simulation strategy, which can effectively complement existing real captured datasets. Second, for better temporal dependency utilization, we develop a Temporally Consistent Enhancer Network (TCE-Net) that consists of stacked 3D convolutions and 2D convolutions to exploit spatial-temporal clues in videos. Last, the temporal dynamic feature dependencies are exploited to obtain consistency constraints for different frame indexes. All these efforts are powered by a Spatial-Temporal Compatible Learning (STCL) optimization technique, which dynamically constructs specific training loss functions adaptively on different datasets. As such, multiple-frame information can be effectively utilized and different levels of information from the network can be feasibly integrated, thus expanding the synergies on different kinds of data and offering visually better results in terms of illumination distribution, color consistency, texture details, and temporal coherence. Extensive experimental results on various real-world low-light video datasets clearly demonstrate the proposed method achieves superior performance to state-of-the-art methods. Our code and synthesized low-light video database will be publicly available at https://github.com/lingyzhu0101/low-light-video-enhancement.git.

The Effects of Eye Illuminance Distribution in the Horizontal Field of View on Human Performance in a Home Paper-Based Learning Situation

Previous studies have focused on task/ambient illumination for visual effects and eye illumination for non-visual effects. In this context, eye illumination within the non-visual realm was defined as vertical non-visual eye illuminance. Considering the functional specificity of central vision and peripheral vision, this study aims to explore whether the distribution of eye illuminance in the horizontal field of view (FOV) affects human performance in home paper-based learning settings. In this study, a within-subject design was used to investigate the effects of eye illuminance distribution on mental perception, task performance, and physiological health while maintaining constant task illuminance and correlated color temperature (CCT). The findings revealed that eye illuminance and its distribution in the horizontal FOV had complex effects on visual fatigue, Landolt ring performance, heart rate variability, and luminous environment appraisal. A relatively optimal lighting configuration was suggested—Scene 4, which was characterized by an eye illuminance level in central FOV of 186 lx and an “m” shaped eye illuminance distribution pattern. This indicates the importance of considering eye illuminance distribution in the horizontal FOV, rather than solely focusing on vertical eye illuminance.

Integrating physical model and image simulations to correct topographic effects on surface reflectance

Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface reflectance images. In contrast with traditional methods, the proposed approach explicitly estimates the illumination distribution over rugged terrains based on image information and then corrects the topographic effects following physical laws. The proposed method was validated and compared with existing well-performed topographic correction methods, including path length correction (PLC), sun-canopy-sensor correction with c factor (SCSC), C correction (CC), and statistical empirical correction (SE). Simulated and satellite data obtained at different times and geolocations are used for evaluation and comparison. The results demonstrated that most existing methods face challenges in removing the biases induced by topography in surface reflectance images. The PLC method failed to obtain reasonable results for faint illumination conditions when the sun zenith angle is high. SE showed relatively poor performance in terms of physical consistency and outlier percentage. Besides, all the traditional methods failed in the correction of cast shadow regions. Comparably, our method consistently demonstrated superior performance in physical consistency, cast shadow correction, and outlier percentage across various regions, encompassing different sun zenith angles and illumination conditions. The proposed method offers an effective and physically consistent approach for the topographic correction of surface reflectance images, facilitating multi-temporal applications over mountainous areas.

Assessment of the visibility of unprotected road users in pedestrian crossing

The article presents selected results of research on improving pedestrian traffic safety. Based on annually-updated accident statistics made available by the police, as well as the new pedestrian traffic regulations in force, detailed work was undertaken to assess the level of visibility of pedestrians by drivers in pedestrian crossing areas. The research was carried out by analyzing several characteristic cases of pedestrian crossings occurring in Poland, in which there was only dedicated lighting for crossings, only street lighting, and a variant of coexistence of both of the above lighting solutions. Illuminance measurements were made in the horizontal and vertical planes of pedestrian crossings, and the results were confronted with the relevant guidelines. The next step involved a complementary measurement of the luminance distribution of the vertical plane containing the pedestrian and a portion of the sub- and super-horizontal background. Visibility pedestrians was considered in positive and negative contrast variants, and was then related to the obtained results of the illumination distribution. The analysis of the results of the study indicated the possibility of limited visibility of pedestrians at the crossings despite the satisfactory results obtained from measurements of the illuminance distribution within the crossings.

Investigating the influence of perforated façade skins on indoor illuminance level: a case study

ABSTRACT Building shading systems is crucial to both avoid indoor overheating during summer and control daylight inflow. This is particularly relevant in exposed glazed façade during summer, where the risk of excessive solar radiation and light entering the building is elevated. The study proposes the use of Dialux software to support the design of sun-shading devices with the two-fold objective of reducing the risk of glare along the façade side and avoiding a dark effect on the opposite side. The procedure is based on a multiple-design approach that simulates the effect of diverse shading panel configurations on indoor illuminance distribution. Then the procedure is implemented on a residential case study in Bologna (Italy) resulted in having a limited depth and thus a high risk of excessive illuminance. The results demonstrate that Dialux is sensitive to changes in the openness factor and geometry of the panel. The multiple simulations allowed the optimal illuminance level of around 300 lux for more than 70% of the case study room surface while containing the area exceeding the optimal level under 1000 lux to less than 20% and to avoid reaching the minimum threshold of 100 lux through a shading system with Openness Factor ranging between 11 and 15%. The workflow can serve not only as a tool for addressing and verifying the design choices but as a design support means itself. Due to its structure and easiness of inputting data and modelling stage, the methodology has a high replicability potential.

RadiantVisions: Illuminating Low-Light Imagery with a Multi-Scale Branch Network

In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions.

A lightness-aware loss for low-light image enhancement

Current low-light image enhancement methods have made great progress on improving the visibility of low-light images. Nevertheless, they pay less attention to preserving visual naturalness and therefore often introduce over-enhancement and local artifacts into their results. To address this issue, it is useful to introduce additional multi-view information of an image into enhancement models, such as illumination distribution. In this context, we propose a simple but effective loss term that expects the originally bright regions in input images and their corresponding enhanced images to be as similar as possible. Via fully exploring the illumination distribution of an image, the loss term makes enhancement models to know which regions should be preserved during training. Therefore, the unnatural effects in output images can be effectively relieved. In our experiments, we incorporate our loss term into several recently proposed low-light image enhancement models. The experimental results on multiple datasets show that over-enhancement and local artifacts can be effectively suppressed by using our loss term.