Sufficient Illumination Research Articles

Flotation froth image enhancement based on region decomposition and guided filtering

Extraction of information from froth images is important for automatic control of froth flotation. However, images captured by cameras often suffer from severe uneven lighting, which significantly reduces the quality of froth images. Low-quality images hinder the accurate extraction of froth information, thereby affecting the control of the froth flotation system. Hence, we propose an image enhancement method based on region decomposition and guided filtering to improve the quality of images. Initially, we separate the image into regions with sufficient and insufficient illumination based on reflectance. In regions with insufficient illumination, the guided filter is applied to direct pixels to acquire information from brighter points in their neighborhood. Conversely, in other regions, we regulate the magnitude of pixel variations to prevent overexposure. Finally, a detail enhancement method is proposed based on a multi-scale Gaussian pyramid and texture fusion to improve clarity and naturalness. The experiments show that the method we proposed surpasses several state-of-the-art algorithms on public datasets. In the field of flotation, our method effectively enhances the image quality. Compared to other enhancement methods under the same segmentation strategy, our method significantly improves segmentation accuracy, demonstrating its strong practical value. In addition, our method also shows advantages in terms of computational speed.

In-line PIV Using a Schlieren System

"The article describes recent tests and developments of PIV recording and evaluation techniques that take advantage of strong forward scattering of tracer particles by in-line recording set ups. By collimating the light of a low-power light source between a pair of schlieren mirrors the observation area is increased to more than 350 mm. The same setup allows for schlieren recording of density gradients. The in-line imaging arrangements have a relatively high background intensity level on which brighter particle images can be detected. Ideally this background is constant in time such that it can be removed through subtraction. The use of forward scattering has a significant influence on the required pulse energy of the light source used. Typically, sufficient illumination is achieved with about 1% of the pulse energy that would normally be used for a conventional orthogonal PIV arrangement employing a laser light sheet. "

Devising a technique for descending the illumination elements of aerial vehicles

The object of this study is the process of descending illumination elements equipped with a braking device in the form of two-bladed grills rotating in different directions. The classic parachute method does not provide the necessary speed of descent, it has low illumination parameters and significant drift of illumination elements by side wind. To solve the tasks set, mathematical dependences were obtained for calculating the aerodynamic characteristics of the descent device with the illumination element and its delivery to the ejection point. The drag and lift force coefficients during the flow around the blades of a dual-rotor impeller with different Reynolds numbers were determined by the method of numerical modeling based on the ANSYS CFX software package. The optimal geometric characteristics of the profile satisfying the condition for the necessary speed of descent of the illumination element at the given weight of the descent apparatus were determined. Reasonable requirements for illumination parameters and an improved composition of the flare have been proposed. A mathematical model of the movement of a body of variable mass to the point of ejection of the illumination element was built. The new design of the descent device makes it possible to reduce the speed of descent by 10–15 % and increase the weight of the payload by 20–30 %. The proposed illumination composition provides sufficient illumination of the object for 5 minutes with a light intensity of 2–2,5 million candelas and an average diameter of the illuminated area of 2000–2500 m. The mathematical model of the movement of a variable mass body to the point of the illumination element ejection makes it possible to determine with high accuracy the gun firing settings with illumination ammunition (30–40 % more accurate) and the time of ejection of illumination elements. Results of the current research make it possible to solve the scientific problem of ensuring the maximum efficiency of illuminating the terrain at night

Design and management considerations towards productively sustainable work places in Saudi Arabia

Purpose This paper aims to review and assess the sustainable design and management considerations affecting workplaces’ productivity in Saudi Arabia. Design/methodology/approach The study uses a mixed approach, commencing with literature review, development and pilot-testing of a structured questionnaire instrument to assess design and management considerations towards productively sustainable workplaces (PSWs). In total, 31 considerations affecting sustainable workplaces’ productivity were identified and clustered under five main directions. A collective group of stakeholders, including architects/engineers (A/E), workplace users and facilities managers, were approached to correlate and compare their individual assessment of the identified PSWs. The relative importance index (RII) for each of the PSW considerations is correlated and discussed. Findings A high level of agreement is recognized among the three stakeholder groups for PSWs considerations’ assessed rankings. The provision of sufficient ventilation and illumination levels, availability of occupational health, security and safety, availability of ergonomically oriented technological infrastructure, availability of formal and informal meeting spaces, efficient space utilization and ergonomic workstations are all ranked highest in importance as design considerations towards PSWs. Originality/value The study stems from the need to understand how the PSWs’ design and management considerations are perceived by all streams of stakeholders. The findings identify the considerations’ importance for prioritization on PSWs’ investments.

Enhancing Facial Recognition Accuracy in Low-Light Conditions Using Convolutional Neural Networks

Facial recognition technology has become increasingly everywhere in various domains, from security and surveillance to personal device authentication. However, its effectiveness can be significantly hindered in low-light conditions, where images often lack sufficient illumination for accurate recognition. This study proposes a novel approach to enhance facial recognition accuracy in low-light conditions using Convolutional Neural Networks (CNNs), Deep Retinex Decomposition Network (DRDN), and CenterFace algorithm. The methodology leverages CNNs for robust feature extraction, while DRDN corrects illumination by decomposing images. CenterFace integrates feature fusion and denoising layers for discriminative facial features and noise mitigation. Experimental results demonstrate a remarkable improvement in recognition performance, exceeding 80% accuracy. This approach showcases the potential of CNN-based methods with advanced techniques to enhance reliability in real-world facial recognition applications, particularly in low-light environments.

Multi-frame demosaicing for the Quad Bayer CFA in the color difference domain

Recently, imaging systems with submicron image sensors have been increasingly adopting the Quad Bayer color filter array (CFA) owing to its high sensitivity under low illumination conditions when pixel binning techniques are employed. Conversely, under sufficient illumination conditions, direct demosaicing methods are required for the Quad Bayer color filter array to obtain a full-resolution color image. However, the Quad Bayer color filter array is more susceptible to aliasing artifacts because of its structural disadvantages compared to the Bayer color filter array. In this paper, we propose the multi-frame demosaicing method to address this problem. The proposed method assesses local spatial contributions within local windows at each pixel to reconstruct the lost spatial resolution caused by aliasing. Additionally, we utilize the color difference domain to exploit inter-channel correlations, aiming to alleviate color artifacts and enhance spatial resolution. The experimental results demonstrate that our proposed method outperforms other methods, both quantitatively and qualitatively, including single-frame-based demosaicing methods and another multi-frame demosaicing method with pixel binning techniques.

A robust autofocusing method for microscopic imaging based on an event camera

Autofocusing is a crucial method for enhancing the efficiency of biological detection workflows. Traditional methods involve calculating various focus metrics on images captured by standard frame-based cameras at multiple static positions to determine the focal point, which can be time-consuming. In contrast, event cameras have the capability to asynchronously detect brightness changes within the field of view of the microscope and provide a continuous stream of events triggered by axial motion. A completely automated coarse-to-fine method is proposed for event based autofocusing, independent of the objective-sample distance. Two criteria for autofocusing in sufficient and low illumination are respectively proposed. The results show that the dynamic accuracy and dynamic repeatability is less than 5 mm.

Побудова математичних моделей збудження хрестоподібних поверхневих хвиль у воді в «співаючих бокалах» при випромінюванні бокалами звукових хвиль

The appearance and structure of cross-shaped waves on the free surface of the liquid contained in the ‘singing glass’ is explained. It was found that the main mode of vibration of the glass is a forced wave, with four nodes evenly spaced along the cylindrical glass wall. When such a singing glass is partially filled with a liquid (which changes the frequency of sound), one can observe another amazing feature: a complex pattern of waves on the free surface of the liquid. With sufficient illumination, one can observe waves directly caused by the vibration of the wall in the four-node mode moving in the circumferential direction at the same speed as the moving finger. However, this picture is dominated by so-called ‘cross waves’ or ‘edge waves’, the crests of which are perpendicular to the vibrating glass wall. Although cross-shaped waves were described by Faraday (1831), until now there was no theoretical justification for the possibility of their excitation in a ‘singing glass’. This paper fills this gap: the appearance of cross waves in a ‘singing glass’ has been mathematically described as resonant oscillations. Non-linear parametric equations describing them were obtained for cross-shaped waves. Two mathematical models were built. The first theoretical model describes the appearance of cross-shaped waves, where only one eigenmode is present on the free surface of the liquid, and the second model applies to the situation where at least two or three eigenmodes are present. The oscillations of the free surface in approximations with three eigenmodes reflect the main features of the wave patterns observed experimentally in the ‘singing glass’.

Natural lighting of interiors and sun protection of premises with roof lighting system

The article describes the contemporary issues of natural lighting of interiors of civil and industrial buildings. The major attention is given to the roof lighting system of the buildings in question, as far as this system provides not only increased levels of natural lighting, but sometimes an undesirable heat gains, due to prolonged insolation period and hence, extra solar radiation. It is noted, that this reason leads to design and construct the means of natural lighting in such a way, that to provide interiors with sufficient natural illumination and efficient sun protection at the same time. Some principal conclusions and recommendations were made as a result of analytic studies, presented in the article. For example, it was competently proved that the best solar standing solution for roof lighting installations are sheds, or sawtooth roof structure. The worst solution, to protect interiors from solar overheating are roof skylights. These skylights require extra sun protection in form of jalousies, blinds, etc. The same additional solar shading, though to less extend, is needed to provide additional sun protection for roof monitors.

A solid strategy to realize efficient antibacterial activity on the shade surface of bulk silicon under natural or indoor lighting

The dual threat posed by the transmission of surface-adhered pathogens and the rise of antibiotic resistance has emphasized the urgent demand for biosafe, potent, and broad-spectrum antibacterial surfaces. The application of conventional light-driven antibacterial strategies is hampered by the lack of sufficient illumination of the shade surface. To address this issue, a novel efficient antibacterial design satisfying the shade-side surface of bulk silicon continuous antibacterial under natural light, indoor ambient room lighting, or dark and dank environments has been developed by exploring synergistic antibacterial mechanisms. Herein, the p-n junction design is employed as the illuminating surface to efficiently transport the photogenerated holes to the shade-side surface, causing considerable photo-electrical injury to the bacteria, while the optimized nanowire structure fabricated on the shade-side surface by facile methods can simultaneously exacerbate the mechanical rupture of the bacteria. As a result, the shade-side surface prepared via the facile method exhibited synergistic broad-spectrum antibacterial behavior with 100 % inactivation against multidrug-resistant Klebsiella pneumoniae and multidrug-resistant Enterococcus. It is worth emphasizing that this work provides a promising strategy to endow the shade surface of bulk silicon with efficient antibacterial activity for real applications.

Mapping the mantle transition zone discontinuities across South-Central Europe using body waves from seismic noise correlations

We map the mantle transition zone (MTZ) discontinuities beneath South-Central Europe using P-wave reflection phases recovered from noise correlations. We analyze up to four years of seismic noise recordings from 900 broadband stations in the study area. By stacking noise correlations in selected summer months, we significantly improved the retrieval of typically low-amplitude body-wave reflection phases in the light of a quiet surface-wave environment and sufficient noise body-wave illumination from deep paths. We obtain reliable P-wave reflections associated with the 410 km and 660 km discontinuities in the period band of 4–10 s. These short-period reflections reveal clear lateral depth variations of the two discontinuities, indicating complex MTZ structure under the greater Alpine region. This provides new important constraints on present and past mantle dynamics, and it shows the strong potential of passive seismic imaging for studying deep-Earth discontinuities.

MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions

The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today’s generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%.

Features of the joint use of visible-range satellite and satellite radar data for monitoring arctic icebergs based on data from Franz Josef Land area

Given the availability of data, in order to monitor icebergs in the vast Arctic region, it is necessary to ensure an optimal ratio of the satellite radar data cost (with a wide swath and high spatial resolution) and the reliability of the results obtained. The aim of the study was to evaluate the effectiveness of using only non-commercial satellite information for monitoring icebergs. To achieve this goal, an iceberg detection technique was applied based on statistical criteria of searching for gradient zones in analyzing two-dimensional fields of satellite images. As a source of information, we used high spatial resolution satellite data obtained from non – commercial satellites Sentinel-1 (radar data) and Landsat-8 (visible spectral range data) with a pixel size of 40 × 20 and 15 m, respectively.Through comparison with the data of the visible spectral range of ultra-high resolution, the error was estimated of measuring the size of icebergs using the visible range equipment of the Landsat-8 satellite and the radar of the Sentinel-1 satellite. Based on the analysis of satellite data of the visible spectral range and radar data, the spatial dimensions of icebergs formed by the outlet glaciers of Franz Josef Land n 2020–2022 were estimated. The materials were collected for the period August-September, corresponding to the conditions of minimum ice extent in the Franz Josef Land area and sufficient natural illumination for using the data of the visible spectral range in the Arctic. In total, about 100 satellite images were analyzed. The sources of error in the satellite measurements are discussed, as well as the reasons for the impossibility in some cases to identify Franz Josef Land icebergs on radar images. It is shown that the largest contribution to the iceberg runoff of Franz Josef Land is made by outflow glaciers located on Georg Land, Wilczek Land, Gall, Salisbury and Champa islands. According to the results of the study, it is concluded that radar data with a pixel size of 40 m allow one to obtain essentially reliable information on the spatial distribution of icebergs in the water area. However, it is not possible to obtain a reliable function of the distribution of icebergs by size since most of the icebergs in the Franz Josef Land water area have small and medium sizes and are rarely identified in images of such spatial resolution.

DMD-based compact SIM system with hexagonal-lattice-structured illumination.

In this study, we developed a novel, compact, and efficient structured illumination microscopy (SIM) system, to our best knowledge. A binary hexagonal lattice pattern was designed and implemented on a digital micromirror device (DMD), resulting in a projection-based structured-light generation. By leveraging the combination of the high-speed switching capability of the DMD with a high-speed CMOS camera, the system can capture 1024×1024 pixels images at a 200fps frame rate when provided with sufficient illumination power. The loading of the hexagonal lattice pattern reduces the number of images required for reconstruction to seven, and by utilizing the DMD modulating characteristics on the illumination path, there is no need to use bulky mechanical structures for phase shifting. We designed a compact system with 110m m×150m m×170m m dimensions that displayed a 1.61 resolution enhancement for fluorescent particle and biological sample imaging.

Multiple Graph Affinity Interactive Network and a Variable Illumination Dataset for RGBT Image Salient Object Detection

Salient object detection (SOD) of images refers to simulating the attention mechanism of human vision to capture the most attractive objects in an image. Current SOD mainly relies on RGB images captured by optical cameras. However, existing optical cameras are not comparable to the human visual system, especially in poorly illumination scenes. Our human visual system is able to resolve scenes well in low light conditions, while optical cameras can barely image without enough illumination. To make machine vision closer to the imaging of the human eye, we propose to use thermal infrared (T) images to compensate RGB images and build a variable illumination RGBT dataset named VI-RGBT1500 for SOD. This dataset is collected under three different illumination conditions including sufficient illumination, uneven illumination and insufficient illumination to fully demonstrate the superiority of the RGBT image combination. Furthermore, we propose a multiple graph affinity interactive (MGAI) network to validate the proposed dataset. Our network structure is simple using only the MGAI to fuse the features of different modalities. Meanwhile, the MGAI model highlights valuable information during the interaction, which facilitates feature representation under variable illumination. The proposed VI-RGBT1500 dataset and three publicly available RGBT SOD datasets are used for the comparison experiments, and the results with the state-of-the-art methods prove that our VI-RGBT1500 dataset is valuable and the performance of the MGAI network is competitive. The VI-RGBT1500 dataset and the MGAI network are available at: https://github.com/huanglm-me/VI-RGBT1500.

The Effect of Vertical Skylights Designed in Buildings on Daylight Illumination

The fact that lighting plays an important role in the energy consumed by buildings, which have a large share in the total energy consumption in the world, requires more work to be done on daylight, which reduces the need for artificial lighting, saves energy in buildings and increases indoor comfort as well. Whether the current energy resources will exist in the future and the extent of the damage to the ecosystem as a result of the deterioration of the natural balance are discussed and discussed from different perspectives. At this point, with the thought of efficient use of energy, studies are carried out to reduce energy consumption and ensure sustainability in the field of lighting. In the building sector, which has a significant share in energy consumption, energy efficient design and sustainable architecture, which aims to use renewable energy sources and reduce energy consumption, are supported by planning and design criteria.
 In this study, the efficiency of skylights, which is one of the lighting methods with daylight, was investigated in accordance with the criteria specified in the relevant zoning legislation and the daylight standard in buildings. The effect of skylights on visual comfort and daylight performance in residential interiors that do not receive direct sunlight was analyzed with 3 different scenarios on spaces in different directions and floors with the help of the Design Builder simulation program. As a result of the analysis, it was concluded that the size of the spaces illuminated by the skylight, the length of the facade where the skylight is located, the floor where the space is located and its direction significantly affect the level of illumination. In line with the findings obtained in the study, it has been observed that the minimum skylight values given in the Planned Areas Zoning Regulation in force today do not provide sufficient daylight illumination, especially in the spaces on the ground floor. For this reason, solutions have been proposed to reach the level of visual comfort in spaces.

Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations

Particle matter (PM) mass concentrations have an important influence on human and environmental health. Lidar plays an important role in the monitoring of PM concentrations. However, the accuracy of PM concentrations retrieved via lidar depends on the performance of the conversion model from the aerosol extinction coefficient (EC) to PM concentration. Therefore, surface PM concentrations, aerosol EC and five meteorological factors are used to build the conversion model that can also be applicable to lidar for retrieving PM concentrations. In this study, the traditional linear model (LM), random forest (RF) and artificial neural network (ANN) algorithms are used to estimate the mass concentrations of PM with aerodynamic diameters < 1 µm (PM1), 2.5 µm (PM2.5) and 10 µm (PM10). The influence of meteorological factors on the conversion model is analyzed. The results show that the meteorological parameters play a non-ignorable role in the model of PM retrieval based on EC, especially when retrieving PM10. Moreover, the performance of three models is investigated by comparing with the surface measurements. The results indicate that the RF and ANN models are more suitable to estimate PM than the LM model. The diurnal variations in mean relative error (MRE) from the three models are then analyzed. There is a diurnal pattern in MRE values, meaning that the maximum values occur in the afternoon and the minimum values occur at night. In addition, there are subtle differences in performance between two machine learning (ML) models. After analysis, it is found that for PM10, the RF method is superior to the ANN when the EC value is small, while the ANN method is superior to the RF when the EC value is relatively high, and the EC threshold is set to 0.6 km−1. For PM1 and PM2.5 estimation, the ANN is the most appropriate model. Finally, accurate diurnal variations in PM1 and PM2.5 based on the ANN model and PM10 based on the combined model of RF and ANN (named RA) are investigated. The results exhibit that the daily maximum values of PM1, PM2.5 and PM10 in the Wuhan area all occur at approximately 08:00–10:00 local time (LT), which is mainly due to the impact of commuter vehicle emissions and the impact of secondary photochemistry response aggravated by sufficient illumination and temperature rises after sunrise. These research results provide an important basis for particulate matter monitoring.

A Line Segment Detector for Space Target Images Robust to Complex Illumination

The relative pose estimation of the space target is indispensable for on-orbit autonomous service missions. Line segment detection is an important step in pose estimation. The traditional line segment detectors show impressive performance under sufficient illumination, while it is easy to fail under complex illumination conditions where the illumination is too bright or too dark. We propose a robust line segment detector for space applications considering the complex illumination in space environments. An improved two-dimensional histogram construction strategy is used to optimize the Otsu method to improve the accuracy of anchor map extraction. To further improve line segment detection’s effect, we introduce an aggregation method that uses the angle difference between segments, the distance between endpoints, and the overlap degree of segments to filter the aggregation candidate segments and connect disjoint line segments that probably came from the same segment. We demonstrate the performance of the proposed line segment detector using a variety of images collected on a semiphysical simulation platform. The results show that our method has better performance than traditional line segment detectors including LSD, Linelet, etc., in terms of line detection precision.

Low-Illumination Image Enhancement Based on Deep Learning Techniques: A Brief Review

As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. Nevertheless, recent advances in this area are dominated by deep-learning-based solutions, and consequently, various deep neural networks have been proposed and applied to this field. Therefore, this paper briefly reviews the latest low-illumination image enhancement, ranging from its related algorithms to its unsolved open issues. Specifically, current low-illumination image enhancement methods based on deep learning are first sorted out and divided into four categories: supervised learning methods, unsupervised learning methods, semi-supervised learning methods, and zero-shot learning methods. Then, existing low-light image datasets are summarized and analyzed. In addition, various quality assessment indices for low-light image enhancement are introduced in detail. We also compare 14 representative algorithms in terms of both objective evaluation and subjective evaluation. Finally, the future development trend of low-illumination image enhancement and its open issues are summarized and prospected.

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics.

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.