Low Illumination Conditions Research Articles

Impact of microalgae culture conditions over the capacity of copper nanoparticle biosynthesis

The biosynthesis of metallic nanoparticles (NPs) has been previously reported using a variety of organic molecules produced by microalgae. However, the results obtained could vary due to the metabolic responses that microalgae have to different culture conditions which could affect the characteristics of the produced nanoparticles. In the present report, copper nanoparticle formation was evaluated by the microalgae Chlorella kessleri, Dunaliella tertiolecta, and Tetraselmis suecica, developed under combined conditions of low (L−) and high (L+) illumination, with low (S−) and high salinity (S+). The illumination was 12 h:12 h light/dark. NP formation was evaluated 72 h after exposure to copper salt. Cupric oxide (CuO) NPs were detected spectrophotometrically in both the culture media (extracellular NPs) and cells (intracellular NPs) of Ch. kessleri with absorbance in the range of 200 to 235 nm. Metallic copper NPs (Cun) were detected with an absorbance between 540 and 560 nm in treatments with cells of C. kessleri and D. tertiolecta which were grown in L+S−, while T. suecica cells showed Cun NPs formations in L−S−, L−S+, and L+S−. The size difference of the NPs was measured by scanning electron microscopy (SEM), in treatments with cells of C. kessleri, ranging in size from 15 to 25 nm (L−S−) and 55 to 65 nm (L+S−). In treatments with culture media, sizes from 35 to 45 nm (L−S−) of NPs were obtained. Differences in the biosynthesis of Cu-based NPs are possible, depending on the culture conditions and the strain of microalgae to be utilized.

Online Non-Negative Multi-Modality Feature Template Learning for RGB-Assisted Infrared Tracking

Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal $\alpha $ value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s).

Segregation-free bromine-doped perovskite solar cells for IoT applications.

Perovskite solar cells have attracted much attention as next-generation solar cells because of their high efficiency and low fabrication costs. Moreover, perovskite solar cells are a promising candidate for indoor energy harvesting. We investigated the effect of bandgap tuning on the characteristics of triple cation-based perovskite solar cells under fluorescent lamp illumination. According to the current density–voltage curves, perovskite solar cells with a wider bandgap than the conventional one exhibited improved open-circuit voltage without sacrificing short-circuit current density under fluorescent lamp illumination. Moreover, the wider bandgap perovskite films including a large amount of bromine in the composition did not show phase segregation, which can degrade the photovoltaic performance of perovskite solar cells, after fluorescent lamp illumination. Our results demonstrate the facile strategy to improve the performance of perovskite solar cells under ambient lighting and great potential of perovskite solar cells for indoor applications such as power sources for the internet of things.

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model.

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

Learning in the dark: 3D integral imaging object recognition in very low illumination conditions using convolutional neural networks

We propose a framework for three-dimensional (3D) object recognition and classification in very low illumination environments using convolutional neural networks (CNNs). 3D images are reconstructed using 3D integral imaging (InIm) with conventional visible spectrum image sensors. After imaging the low light scene using 3D InIm, the 3D reconstructed image has a higher signal-to-noise ratio than a single 2D image, which is a result of 3D InIm being optimal in the maximum likelihood sense for read-noise dominant images. Once 3D reconstruction has been performed, the 3D image is denoised and regions of interest are extracted to detect 3D objects in a scene. The extracted regions are then inputted into a CNN, which was trained under low illumination conditions using 3D InIm reconstructed images, to perform object recognition. To the best of our knowledge, this is the first report of utilizing 3D InIm and convolutional neural networks for 3D training and 3D object classification under very low illumination conditions.

An anisotropic heat diffusion model for enhancing the extraction of underground rail track fasteners under extremely low and uneven illumination conditions

During the past two decades, subway systems have become one of the most dominant infrastructural developments in China at an unprecedented pace and scale. More than 60 metro lines in 25 cities have been completed, transporting more than 70 million passengers daily. Operating the subway systems safely and efficiently is a continuously pressing demand from both the management companies and the public. Although many automated or semi-automated methods for extracting critical components of the rail track systems, e.g. rail, fastener, sleeper, etc., have significantly improved the productivity of routine inspection, the unique challenges posed by the subway systems have hindered these existing methods from successful implementation because of the extremely low illumination in the underground environment, whereas additional artificial lighting often poses extremely uneven illumination. In this study, a generalized local illumination adaptation model using an anisotropic heat equation is proposed to dynamically adjust the acquired rail track images with extremely low and uneven illumination conditions. An integration flow is then proposed to seamlessly incorporate the proposed model into the state-of-the-art automated fastener detection algorithms. The results show that the proposed local illumination adaptation model can significantly improve the performance of the tested state-of-the-art fastener detection algorithms when they are applied to the images collected in the environment with extremely low and uneven illumination conditions, e.g. subway systems.

Removal of reflections in LWIR image with polarization characteristics.

Long-wave infrared (LWIR) imaging has been successfully used in surveillance applications in low illumination conditions. However, infrared energy reflected from smooth surfaces such as floors and metallic objects may reduce object detection and tracking accuracies. In this paper, we present a novel reflection removal method using polarization properties of the reflection in LWIR imagery. Reflection can be distinguished from the scene by two unique characteristics of polarization: the difference of two orthogonal polarized components (OPC) and the uniformity of angle of polarization (AoP). The OPC difference helps locate the regions of reflection. The uniformity of AoP in the reflection region pose a strong constraint for reflection detection. The proposed joint reflection detection method combines the OPC difference and the uniformity of AoP can detect actual reflection region. Then the closed-form matting method improves the robustness of the method and removes the reflection from the scene. Experiment results demonstrate that the proposed scheme effectively removes the reflection in challenging situations where many existing techniques may fail.

The Effect of Light Intensity, Temperature, and Oxygen Pressure on the Photo-Oxidation Rate of Bare PbS Quantum Dots.

The oxidation speed of PbS quantum dots has been a subject of controversy for some time. In this study, we reveal the precise functional form of the oxidation rate constant for bare quantum dots through analysis of their photoluminescence as a function of temperature, oxygen pressure, and excitation-laser intensity. The combined effect of these factors results in a reduced energy barrier that allows the oxidation to proceed at a high rate. Each absorbed photon is found to have a 10−8 probability of oxidizing a PbS atomic pair. This highlights the importance of photo-excitation on the speed of the oxidation process, even at low illumination conditions. The procedure used here may set up a quantitative standard useful for characterizing the stability of quantum dots coated with ligands/linkers, and to compare different protection schemes in a fair quantitative way.

Spatial-temporal human gesture recognition under degraded conditions using three-dimensional integral imaging.

We present spatial-temporal human gesture recognition in degraded conditions including low light levels and occlusions using passive sensing three-dimensional (3D) integral imaging (InIm) system and 3D correlation filters. The 4D (lateral, longitudinal, and temporal) reconstructed data is processed using a variety of algorithms including linear and non-linear distortion-invariant filters; and compared with previously reported space-time interest points (STIP) feature detector, 3D histogram of oriented gradients (3D HOG) feature descriptor, with a standard bag-of-features support vector machine (SVM) framework, etc. The gesture recognition results with different classification algorithms are compared using a variety of performance metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), SNR, the probability of classification errors, and confusion matrix. Integral imaging video sequences of human gestures are captured under degraded conditions such as low light illumination and in the presence of partial occlusions. A four-dimensional (4D) reconstructed video sequence is computed that provides lateral and depth information of a scene over time i.e. (x, y, z, t). The total-variation denoising algorithm is applied to the signal to further reduce noise and preserve data in the video frames. We show that the 4D signal consists of decreased scene noise, partial occlusion removal, and improved SNR due to the computational InIm and/or denoising algorithms. Finally, gesture recognition is processed with classification algorithms, such as distortion-invariant correlation filters; and STIP, 3D HOG with SVM, which are applied to the reconstructed 4D gesture signal to classify the human gesture. Experiments are conducted using a synthetic aperture InIm system in ambient light. Our experiments indicate that the proposed approach is promising in detection of human gestures in degraded conditions such as low illumination conditions with partial occlusion. To the best of our knowledge, this is the first report on spatial-temporal human gesture recognition in degraded conditions using passive sensing 4D integral imaging with nonlinear correlation filters.

Real-Time Inter-Frame Histogram Builder for SPAD Image Sensors

CMOS image sensors based on single-photon avalanche-diodes (SPAD) are suitable for 2D and 3D vision. Limited by uncorrelated noise and/or low illumination conditions, image capturing becomes nearly impossible in a single-shot exposure time. Moreover, the depth accuracy is affected by jitter. Therefore, many frames need to be taken to reconstruct the final accurate image. The proposed reconstruction algorithm is based on pixel-wise histogram building. Specifically, a histogram is built on the fly for each pixel of the array from the ongoing acquired frames. This paper presents the design and implementation on FPGA of a real-time pixel-wise inter-frame histogram builder at 1 kfps. The design has been proven with a 64 × 64-pixels SPAD camera. Its remarkable robustness has been demonstrated in harsh conditions, such as 42 kHz of dark count rate (DCR) and high background illumination up to 20 times larger than the DCR. The system has a graphic user interface for 2D/3D imager configuration, image streaming, and pixel-wise histogram streaming.

Hydrogenation and Structuration of TiO2 Nanorod Photoanodes: Doping Level and the Effect of Illumination in Trap-States Filling

Both electronic properties and light absorption are key features in materials engineering to achieve efficient photoelectrodes for water splitting. Adjusting the potential drop inside the nanostructured semiconductor material through modification of the electronic properties is mandatory to drive efficiently the photogenerated charges. In this work, a hydrogen reduction treatment on titanium dioxide rutile nanorod based photoanodes has been performed in order to adjust the donor density to maximize electron–hole separation. Also, incident photon-to-current efficiency (IPCE) measurements and the effect of a light bias have been elucidated, finding relevant differences in low illumination conditions due to nonfilled trap states. A physical model is proposed to show the role of the donor density on the overall performance of the photoanodes under study. The obtained productivity was enhanced by structuring the conductive glass substrates where TiO2 nanorods were grown, resulting in a 20% increase of the phot...

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion.

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.

Стимуляция ризогенеза красной и чёрной малин in vitro с использованием когерентного излучения

The effect of supplementary red LED and laser lightning on the rhizogenesis in vitro of plants of the genus Rubus at different levels of the basic polychromatic illumination was studied. The relevance of this work is due to the need to improve the biotechnological methods of accelerated plant propagation at all stages of cloning, including root formation stage. One of the ways to solve this problem is to use coherent radiation to stimulate physiological and morphogenetic processes in plants and to create energy-saving cultivation technologies on the basis of the methods developed. Microcuttings of berry crops cultivated with 1000 and 2000 Lux illumination were subjected to daily supplementary lightning for 20 minutes with LEDs (λ = 638 nm, power density 5 W/m 2 ) or helium-neon laser (λ = 632.8 nm, power density 3 W/m 2 ). In conditions of low illumination (1000 Lux), the intensity of rhizogenesis in red raspberry and black raspberry decreased in comparison with the illumination of 2000 Lux. LED-based and laser additional irradiation increased the intensity of the rhizogenesis of berry crops in vitro. With a low basic illumination (1000 Lux) the maximum stimulating effect of an additional red quasi-monochromatic illumination was observed. In red and black raspberry, laser and LED backlighting with insufficient basic illumination made it possible to obtain the number of roots and their total length per one explant not differing statistically from those at full illumination, but without backlighting.

Incrementally perceiving hazards in driving

Abstract Perceiving hazards on road is significantly important because hazards have large tendency to cause vehicle crash. For this purpose, the feedbacks of more than one hundred drivers with different experience for safe driving are gathered. The obtained feedbacks indicate that the irregular motion behaviour, such as crossing or overtaking of traffic participants, and low illumination condition are highly threatening to drivers. Motivated by that, this paper fulfills the hazards detection by involving motion, color, near-infrared, and depth clues of traffic scene. Specifically, an incremental motion consistency measurement model is firstly built to infer the irregular motion behaviours, which is achieved by incremental graph regularized least soft-threshold squares (GRLSS) incorporating the better Laplacian distribution of the noise estimation in optical flow into the motion modeling. Second, multi-source cues are adaptively weighted and fused by a saliency based Bayesian integrated model for arousing driver’s attention when potential hazards appears, which can better reflect the video content and select the better band(s) for hazards prediction in different illumination conditions. Finally, the superiority of the proposed method relating to other competitors is verified by testing on twelve difficult video clips captured by ourselves, which contain color, near-infrared and recovered depth simultaneously and no registration or frame alignment is needed.

Study on Low Illumination Simultaneous Polarization Image Registration Based on Improved SURF Algorithm

Registration of simultaneous polarization images is the premise of subsequent image fusion operations. However, in the process of shooting all-weather, the polarized camera exposure time need to be kept unchanged, sometimes polarization images under low illumination conditions due to too dark result in SURF algorithm can not extract feature points, thus unable to complete the registration, therefore this paper proposes an improved SURF algorithm. Firstly, the luminance operator is used to improve overall brightness of low illumination image, and then create integral image, using Hession matrix to extract the points of interest to get the main direction of characteristic points, calculate Haar wavelet response in X and Y directions to get the SURF descriptor information, then use the RANSAC function to make precise matching, the function can eliminate wrong matching points and improve accuracy rate. And finally resume the brightness of the polarized image after registration, the effect of the polarized image is not affected. Results show that the improved SURF algorithm can be applied well under low illumination conditions.

Vehicle-logo recognition based on modified HU invariant moments and SVM

As a part of the vehicle identification system, the logo recognition, while matching with the license plate recognition, can be used to define the identity of the vehicle more accurately and provide reliable evidence for the deck car investigation, illegal escape and vehicle tracking. However, it is a difficult problem for the research to position the logos of different vehicles and the identification of the vehicles under low illumination conditions. This paper firstly uses the features of the color of the license plate to locate the license plate, and carries out the rough location of the logo according to the prior knowledge. Then, uses gray level, contrast enhancement, smoothing de-noising, edge detection and background suppression methods to deal with the coarse location of logo and realize the positioning of logo accurately. Next, extracts features of Vehicle-logo according seven HU invariant, considering the influence of low illumination conditions, this paper adds three HU invariant distances and establishes the characteristic library of the logo image. Thirdly, uses the support vector machine(SVM) to identify the logo and Cross validation(CV) methods to optimize the parameter C and g of SVM at the same time. In order to improve the recognition accuracy of the algorithm under low illumination conditions, the Grey Wolf Optimize (GWO) is used to further optimize the kernel function. Finally, takes 9 kinds of common Vehicle-logo as the logo to be identified, uses SVM to train 80% of the samples and test 20% of the samples. The results of experiments show that the increase of the invariant moments feature can obviously improve the accuracy of the logo, GWO is better than CV to improve the accuracy, and the average recognition rate is more than 92%, which effectively solve the problem of Vehicle-logo identification under low illumination conditions.

Three-dimensional object visualization and detection in low light illumination using integral imaging.

Conventional two-dimensional (2D) imaging systems that operate in the visible spectrum may perform poorly in environments under low light illumination. In this work, we present the potential of passive three-dimensional (3D) integral imaging (II) to perform 3D imaging of a scene under low light conditions in the visible spectrum and without the need for a photon counting or cooled CCD camera. Using dedicated algorithms, we demonstrate that the reconstructed 3D integral image is naturally optimum in a maximum likelihood sense in low light levels and in the presence of detector noise enabling object visualization in the scene. The conventional 2D imaging fails due to the limited number of photons. Using 3D imaging, we demonstrate the potential for 3D detection of objects behind occlusion in a photon-starved scene. To the best of our knowledge, this is the first report of experimentally using II sensing under low illumination conditions for 3D visualization and 3D object detection in the presence of obscurations with a conventional image sensor.

INFLUENCE OF THE TOPOGRAPHIC CORRECTION STRATEGY ON THE LAND COVER CLASSIFICATION: A SPECTRAL APPROACH

Topographic correction methods applied to orbital imagery have been evaluated by several authors. The evaluation criteria have been based on the correlation decreasing between shade and reflectance at different spectral bands or, in some cases, it has included the performance of digital classifiers trying to separate specifi c land cover types. Some topographic correction methods include sampling procedures based on the original image to be corrected and results are dependent on how they are conducted. The influence of the topographic correction on the land cover types spectral characterization is frequently neglected although it is relevant, specially in "quantitative remote sensing" in which empirical or physical relations between radiometric data and biophysical or geophysical data are explored. The objective here was to evaluate the classification results achieved from original and topographic corrected images using the C-correction method exploring two sampling procedures. The infl uence of the spectral characterization of some land cover types was also evaluated. Results indicated that pixels under high and low illumination conditions presented bigger spectral dynamics, specially in the infrared spectral region (near and SWIR). Topographic correction based on a single land cover class (vegetation) did not present better classifi cation result when compared to that classifi cation based on the original images. Sampling procedures based on diff erent land cover classes presented lower mapping accuracy and low matching level with the other classification results.

2-10 低照度環境における液晶テレビの好ましい色再現(第2部門 情報ディスプレイ・マルチメディアストレージ・コンシューマエレクトロニクス)

2-10 低照度環境における液晶テレビの好ましい色再現(第2部門 情報ディスプレイ・マルチメディアストレージ・コンシューマエレクトロニクス)