Low Illumination Environment Research Articles

LIASM-NRID: Constructing an atmospheric scattering model for low-light conditions and dehazing nighttime road images

Dehazing of nighttime road images holds significant practical relevance for autonomous driving and intelligent transportation systems operating in nocturnal conditions. To address the limitations of traditional atmospheric scattering models in accurately describing the imaging process in low illumination environments such as nighttime, this study constructs a low illumination environment atmospheric scattering model. Based on this newly constructed model, we propose an algorithm specifically designed for dehazing road images at nighttime. The proposed model incorporates an incident light attenuation factor, an additive noise factor, and a global compensation factor related to the scene's depth. The proposed nighttime road image dehazing algorithm firstly suppresses the interference of additive noise in the original image using wavelet decomposition and soft thresholding operation. Then, a transmittance-solving method containing the primary structural information of the image is designed based on the color attenuation linear model, and the solved transmittance is refined based on the interval gradient image texture structure method. Consequently, the global light map estimation process is formulated as an optimization problem to ensure the accuracy of the atmospheric light value calculation. In the final stages, a novel RGB color equalization method is introduced to address the issue of non-uniform incident light color bias in the dehazed images. Additionally, to eliminate the halo problem in the dehazed images, the adaptive histogram equalization algorithm is modified by incorporating a normalized gamma correction function. The experimental results show that the proposed nighttime road image dehazing algorithm exhibits robust performance and generality across various nighttime scenes. Compared with the representative algorithms for nighttime image dehazing, it can get more excellent dehazing results and has more outstanding performance in all numerical evaluation indexes.

The Effect of Ambient Illumination and Text Color on Visual Fatigue under Negative Polarity.

This study investigates the effects of ambient illumination and negatively polarized text color on visual fatigue, exploring the issue of visual fatigue when using visual display terminals in low-illumination environments. The research methodology utilizes an experimental design to collect data on changes in pupil accommodation and blink rate through an eye tracker. Participants completed a reading task while exposed to various text colors and ambient light conditions to evaluate visual fatigue and cognitive performance. The study's findings suggest that text color significantly affects visual fatigue, with red text causing the highest level of visual fatigue and yellow text causing the lowest level of visual fatigue. Improvements in ambient lighting reduce visual fatigue, but the degree of improvement varies depending on the text color. Additionally, cognitive performance is better when using yellow and white text but worse when using red text. Yellow text is the most effective choice for reducing visual fatigue under negative polarity. Increasing ambient lighting can also improve visual fatigue in low-illumination conditions. These findings will offer valuable guidance for designing visual terminal device interfaces, especially for low-illumination or night environments, to minimize visual fatigue and improve user experience.

AI-Driven Dim-Light Adaptive Camera (DimCam) for Lunar Robots

Abstract. The past decade has been a boom in lunar exploration. China, India, Japan and other countries have successfully landed landers or rovers on the lunar surface (Wu et al., 2014, 2018, 2020; Prasad et al., 2023). Future missions to explore the Moon are focusing on the lunar south pole (Peña-Asensio et al., 2024). The solar altitude angle at the lunar south pole is extremely low, resulting in low solar irradiance and large areas often in dim light or shadows. The permanently shadowed regions (PSRs) at the lunar south pole are also likely containing substantial amounts of water ice (Li et al., 2018). Future lunar robots exploring the lunar south pole will need to operate in low light or shadowed regions, making camera sensors sensitive to the dim-light environments necessary for these robots. Common night vision sensors usually use near-infrared cameras. However, sensors based on passive infrared technology have image resolution limited by several factors, including the intensity of infrared radiation emitted by the object, the sensitivity of the camera, and the performance of the optical system. For instance, thermal imagers typically have a resolution of 388*284 pixels only. We have developed a dim-light adaptive camera (DimCam) that is ultra-sensitive to the varying illumination conditions driven by AI to achieve high-definition imaging of 1080P or above, for future lunar robots operating in shadows or dim-light regions. The DimCam integrates two starlight-level ultra-sensitive imaging sensors connected by a rigid base to provide stereo vision in low illumination environment. An AI edge computing unit is embedded inside the DimCam to adaptively denoise and enhance image quality. The AI module uses an end-to-end image denoising network to identify and remove noises in the images more accurately by utilizing depth information from the stereo sensors. Compared with traditional monocular denoising algorithms, the denoising network based on stereo vision can significantly improve denoising effects and efficiency by enhancing the signal-to-noise ratio of the data input in the front end. The superposition of overlapping scenes can be regarded as a delayed exposure. Concurrently, the residual analysis of the aligned images aids in noise identification. In addition, for pixels obscured by noise, more accurate pixel values can be restored through interpolation or replacement using depth information obtained from the stereo sensors. Subsequently, a pre-trained lightweight deep network modified from Zero-DCE (Guo et al., 2020) is used for image quality enhancement in terms of brightness and contrast, providing high-quality images even in low-light environments for subsequent applications, such as positioning and navigation of robots, 3D mapping of the surrounding environment, and autonomous driven. We have tested the DimCam in a simulated environment in the laboratory, and the results show that the DimCam has promising performances and great potential for various applications.

Detecting and identifying occluded and camouflaged objects in low-illumination environments

One of the prevailing areas of contemporary research involves the differentiation and identification of diverse objects within a given scene through automated systems. The field of study under consideration presents a multitude of obstacles, including but not limited to issues such as diminished lighting conditions, occlusion, and camouflage. The captured image exhibits variations in illumination, resulting in uneven brightness, reduced contrast, and the presence of noise. The fundamental basis of computer vision algorithms lies in the process of extracting features from datasets and subsequently discerning these features through neural networks. The task of extracting distinct feature key points from images captured under low lighting conditions is exceedingly challenging. To address this issue, the present study seeks to employ deep learning models to implement image enhancement techniques specifically designed for low-light conditions. The primary emphasis lies in obtaining key feature points that are differentiable, thereby enabling the utilization of this annotated data for specific tasks such as object detection. The task of identifying occluded and camouflaged objects has been successfully accomplished, yielding an impressive accuracy rate of 93% in total. The mean average precision has been achieved as 85% which is reasonably high compared to many earlier works.

Research on the Anchor-Rod Recognition and Positioning Method of a Coal-Mine Roadway Based on Image Enhancement and Multiattention Mechanism Fusion-Improved YOLOv7 Model

A drill-anchor robot is an essential means of efficient drilling and anchoring in coal-mine roadways. It is significant to calculate the position of the drill-anchor robot based on the positioning information of the supported anchor rod to improve tunneling efficiency. Therefore, identifying and positioning the supported anchor rod has become a critical problem that needs to be solved urgently. Aiming at the problem that the target in the image is blurred and cannot be accurately identified due to the low and uneven illumination environment, we proposed an improved YOLOv7 (the seventh version of the You Only Look Once) model based on the fusion of image enhancement and multiattention mechanism, and the self-made dataset is used for testing and training. Aiming at the problem that the traditional positioning method cannot guarantee accuracy and efficiency simultaneously, an anchor-rod positioning method using depth image and RGB image alignment combined with least squares linear fitting is proposed, and the positioning accuracy is improved by processing the depth map. The results show that the improved model improves the mAP by 5.7% compared with YOLOv7 and can accurately identify the target. Through the positioning method proposed in this paper, the error between the positioning coordinate and the measurement coordinate of the target point on each axis does not exceed 11 mm, which has high positioning accuracy and improves the positioning accuracy and robustness of the anchor rod in the coal-mine roadway.

Nighttime bionic compass based on a short-wave infrared polarization sensing system.

Sky-bionic polar co-ordinate navigation is an effective means of providing navigational information in the absence of a priori information. Polar co-ordinate navigation during clear daytime conditions has been studied, but there has been a lack of research of it at night due to problems with noise. Therefore, in this paper, a short-wave infrared polarimetric sensor system is designed, which is capable of acquiring atmospheric polarimetric information in low illumination environments at night, compared with traditional visible band sensors. Additionally, based on the statistics of polarization angle information, an algorithm for removing noise and starlight is proposed to solve the influence of starlight and noise on the polarization information at night. After many outdoor experiments, we found that the method can output the heading angle stably and accurately, and its standard deviation is controlled to be 0.42° in a clear night.

Fall Detection in Low-Illumination Environments From Far-Infrared Images Using Pose Detection and Dynamic Descriptors

Fall Detection in Low-Illumination Environments From Far-Infrared Images Using Pose Detection and Dynamic Descriptors

TF-YOLO: A Transformer–Fusion-Based YOLO Detector for Multimodal Pedestrian Detection in Autonomous Driving Scenes

Recent research demonstrates that the fusion of multimodal images can improve the performance of pedestrian detectors under low-illumination environments. However, existing multimodal pedestrian detectors cannot adapt to the variability of environmental illumination. When the lighting conditions of the application environment do not match the experimental data illumination conditions, the detection performance is likely to be stuck significantly. To resolve this problem, we propose a novel transformer–fusion-based YOLO detector to detect pedestrians under various illumination environments, such as nighttime, smog, and heavy rain. Specifically, we develop a novel transformer–fusion module embedded in a two-stream backbone network to robustly integrate the latent interactions between multimodal images (visible and infrared images). This enables the multimodal pedestrian detector to adapt to changing illumination conditions. Experimental results on two well-known datasets demonstrate that the proposed approach exhibits superior performance. The proposed TF-YOLO drastically improves the average precision of the state-of-the-art approach by 3.3% and reduces the miss rate of the state-of-the-art approach by about 6% on the challenging multi-scenario multi-modality dataset.

Research on the Application of Convolutional Neural Network Model in Night Surveillance Video Image Enhancement

With the popularization of professional digital imaging equipment, digital image processing is popularly used in many fields such as industrial production, video surveillance, intelligent transportation, remote sensing and monitoring, and plays a significant role. In low illumination environments such as cloudy days, nights, indoor and object occlusion, imaging devices often capture images with low brightness and contrast, severe loss of detail information, and a large amount of noise. Enhancing low illumination images can enhance their clarity, highlight the texture details of the scene, greatly enhance the quality of the image, and provide data quality assurance for completing tasks such as target recognition and tracking, image segmentation, etc. This paper proposes a low light image enhancement algorithm based on CNN model to address the problem of low brightness and unclear monitoring video images in nighttime scenes due to lighting conditions. This algorithm can effectively improve the quality of low light images and exhibit superiority on multiple public datasets. The algorithm proposed in this article not only effectively improves the brightness of the image, but also enhances the detail clarity of the image to a certain degree, and can avoid color distortion and halo phenomena to a certain degree.

Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter.

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep learning and traditional methods like RankIQA, BRISQUE, and SSIM, various partitioning schemes were compared to explore the highest-quality imaging heterogeneous processing scheme. The UNet-specific deep-learning processing unit (DPU) based on a field programmable gate array (FPGA) provided a 14.67× acceleration ratio for the total network and for deconvolution and max pool, the calculation latency was as low as 2.46 ms and 97.10 ms, achieving an impressive speedup ratio of 46.30× and 36.49× with only 4.04 W power consumption. The HISP consisting of a DPU and the FPGA-implemented traditional image signal processor (ISP) submodules, which scored highly in the image quality assessment system, with a single processing time of 524.93 ms and power consumption of only 8.56 W, provided a low-cost and fully replicable solution for edge image processing in extremely low illumination and high noise environments.

Shadow Hunter: Low-Illumination Object-Detection Algorithm

Recently, object detection, which is focused on images with normal illumination levels, has achieved great success. However, the accuracy of object detection is reduced in suboptimal environments due to the images plagued by noise and low contrast. For boosting the performance of object-detection tasks under low-illumination conditions, we propose three modules for improvement: (1) the low-level feature attention (LFA) module learns to focus on the regional feature information of the object in the low-illumination environment, highlighting important features and filtering noisy information; (2) the feature fusion neck (FFN) obtains enriched feature information by fusing the feature information of the feature map after backbone; (3) the context-spatial decoupling head (CSDH) enables the classification head to focus on contextual semantic information so that the localization head obtains richer spatial details. Extensive experiments show that our algorithm realizing end-to-end detection shows good performance in low-illumination images.

Target Localization Method Based on Image Degradation Suppression and Multi-Similarity Fusion in Low-Illumination Environments

Frame buildings as important nodes of urban space. The include high-speed railway stations, airports, residences, and office buildings, which carry various activities and functions. Due to illumination irrationality and mutual occlusion between complex objects, low illumination situations frequently develop in these architectural environments. In this case, the location information of the target is difficult to determine. At the same time, the change in the indoor electromagnetic environment also affects the location information of the target. Therefore, this paper adopts the vision method to achieve target localization in low-illumination environments by feature matching of images collected in the offline state. However, the acquired images have serious quality degradation problems in low-illumination conditions, such as low brightness, low contrast, color distortion, and noise interference. These problems mean that the local features in the collected images are missing, meaning that they fail to achieve a match with the offline database images; as a result, the location information of the target cannot be determined. Therefore, a Visual Localization with Multiple-Similarity Fusions (VLMSF) is proposed based on the Nonlinear Enhancement And Local Mean Filtering (NEALMF) preprocessing enhancement. The NEALMF method solves the problem of missing local features by improving the quality of the acquired images, thus improving the robustness of the visual positioning system. The VLMSF method solves the problem of low matching accuracy in similarity retrieval methods by effectively extracting and matching feature information. Experiments show that the average localization error of the VLMSF method is only 8 cm, which is 33.33% lower than that of the Kears-based VGG-16 similarity retrieval method. Meanwhile, the localization error is reduced by 75.76% compared with the Perceptual hash (Phash) retrieval method. The results show that the method proposed in this paper greatly alleviates the influence of low illumination on visual methods, thus helping city managers accurately grasp the location information of targets under complex illumination conditions.

An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer’s rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.

Enhancement method for high colour polarization region under low irradiance condition

Due to the significant impact of low illumination environments on polarization imaging, polarization information is heavily influenced by noise interference. To address this issue, we propose a fusion method to enhance the high colour polarization area in low irradiance conditions. Firstly, we obtain the colour degree of linear polarization (DoLP) image, the colour angle of polarization (AoP) image, and the colour intensity (S0) image by the colour polarization camera. We normalize the S0 image. Subsequently, we map the colour AoP information in combination with the colour DoLP threshold to the H channel. The visual saliency information and brightness information of the colour DoLP image and the colour S0 image are mapped to the S channel and V channel, respectively. Finally, the data is transferred to the RGB space. Experiments show that our method surpasses several existing methods in both visual quality and quantitative measurement.

Recognition of coal and gangue under low illumination based on SG-YOLO model

ABSTRACT For the low illumination and dust in the coal and gangue identification site environment, which leads to poor recognition, an improved lightweight low-illumination gangue recognition algorithm based on YOLOv5s model is proposed: SG-YOLO algorithm. The original backbone network is replaced by GhostNet, a lightweight network, to optimize the feature extraction structure, reduce the model parameters, and decrease the computational power of the model; the SimAM attention mechanism module is introduced in the head part of the model to enhance the learning ability of coal and gangue features. Experiments show that compared with the YOLOv5s model, the improved model has a mAP of 97.0% on the gangue dataset, which improved by 1.4%. The size of the model is compressed to 55% of the original. The number of parameters is reduced by 47.6%, and the computational effort is reduced by 49.4%. Meanwhile, the recognition accuracy of the improved SG-YOLO model for coal and gangue under low illumination is 96.5% and 98.5% respectively, which effectively improves the recognition accuracy of coal rain gangue under low illumination environment.

A full-time deep learning-based alert approach for bridge–ship collision using visible spectrum and thermal infrared cameras

With the rapid development of transportation infrastructure and the growing water transportation industry, many bridges have been constructed across navigable rivers. However, collisions between ships and bridges remain a frequent occurrence, particularly under low illumination conditions. To address this issue, this paper proposes a full-time bridge–ship collision alert approach based on the combination of visible spectrum and infrared cameras. This approach includes the following steps: (1) capturing and identifying ships in the channel using a single visible spectrum camera is challenging in low illumination environments such as nighttime. To overcome this limitation, we propose the use of infrared thermal imaging technology, which can monitor ships at night due to their inherent emissivity of ship thermal radiation. By comprehensively analyzing factors such as the real-time lighting environment and comparing the advantages of visible spectrum cameras and thermal infrared cameras in object recognition under various illuminance levels, we determine the effectiveness of the decision-making method of the dual-channel camera for ships under different illumination conditions. (2) To detect and track ships in the channel, a deep learning-based automatic detection and tracking method is designed using visible spectrum and infrared thermal imaging cameras, providing early warning of dangerous trajectories of ships that threaten the piers. To address the real-time identification and positioning of ships by dual-channel cameras in different light environments, we adopt a target detection network and a displacement calculation algorithm for multi-target tracking. Analysis results demonstrate that this method can automatically assign visible spectrum and thermal imaging cameras as effective input sources in different illumination environments to detect and warn ships with dangerous trajectories, contributing to the safety of navigable ships and bridges in low illumination conditions.

Cascaded Denoising Transformer for UAV Nighttime Tracking

The automation of unmanned aerial vehicles (UAVs) has been greatly promoted by visual object tracking methods with onboard cameras. However, the random and complicated real noise produced by the cameras seriously hinders the performance of state-of-the-art (SOTA) UAV trackers, especially in low-illumination environments. To address this issue, this work proposes an efficient plug-and-play cascaded denoising Transformer (CDT) to suppress cluttered and complex real noise, thereby boosting UAV tracking performance. Specifically, the novel U -shaped cascaded denoising network is designed with a streamlined structure for efficient computation. Additionally, shallow feature deepening (SFD) encoder and multi-feature collaboration (MFC) decoder are constructed based on multi-head transposed self-attention (MTSA) and multi-head transposed cross-attention (MTCA), respectively. A nested residual feed-forward network (NRFN) is developed to focus more on high-frequency information represented by noise. Extensive evaluation and test experiments demonstrate that the proposed CDT has a remarkable denoising effect and improves UAV nighttime tracking performance.

Unsupervised Change Detection in Wide-Field Video Images Under Low Illumination

In low-illumination environments such as at night, due to factors such as the large monitoring field of an eagle eye, short sensor exposure time, and high-density random noise, the video images collected by image sensors generally have poor visual quality and low signal-to-noise ratio, which makes it difficult for surveillance systems to detect weak changes. To solve this problem, we propose a method for image change detection (CD) in surveillance video based on optimized k-medoids clustering and adaptive fusion of difference images (DIs). First, for the input multitemporal video surveillance images, two DIs are obtained by log-ratio and extremum pixel ratio operators. Then, the two DIs are adaptively fused by combining the local energy of DIs and the Laplacian pyramid. Simultaneously, the fused DI is compressed by the normalization function, and the final DI is obtained via the improved adaptive median filter. Finally, the changed image is obtained by using the optimized k-medoids clustering algorithm. The experimental results show that the proposed method can accurately and effectively detect weak changes in the eagle eye surveillance picture in a low-illumination environment. Compared with those of other methods, the accuracy and robustness of the proposed method are higher, and the running time of the algorithm is shorter. Moreover, it will not generate a false alarm due to the influence of noise in unchanged scenes.

Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario

In the field of automatic driving, clear image is one of the important prerequisites for the decision-making system relying on computer vision to make correct decisions. Rainy days and low illumination environment are two common scenes that seriously affect image quality. Researchers have proposed many effective methods for these two research fields. However, few applications have been studied in the field of automatic driving comprehensively. To solve the problem, a low illumination image enhancement method in rainy days is proposed, which combines the two scenes and can be applied to the field of automatic driving. This algorithm takes good advantage of the fact that raindrops cannot appear in the same frame at different times to eliminate the noise of raindrops in the image .And its main process is to fuse continuous image frames by maximum method through multi-scale Gaussian filtering. Thus, the fused image is used as the illumination map and the original image to be denoised is used as the input of Multi-Scale Retinex(MSR) method.Finally, the reflection image which can be directly applied in automatic driving is obtained.