Nighttime Images Research Articles

Identification and Removal of Light Pollution in Maritime Night-Time Images Based on Spatial Frequency Blocks

The safety of ships during nighttime navigation has always been a major concern. With the widespread application of technologies such as intelligent recognition, intelligent detection, and unmanned ship navigation at night, nighttime maritime light pollution has significantly affected the effectiveness of these intelligent technologies and navigation safety. Therefore, effectively eliminating nighttime maritime light pollution has become an urgent challenge that needs to be addressed. This paper presents a model based on spatial frequency blocks (SFBs) to solve the problem of light pollution in nighttime sea images. The model includes ResNet-50, an encoder, a decoder, and a discriminator. To enable the model to better remove the influence of light pollution, this study designs a method of first detecting the light pollution area and then removing it. It extracts image information from the space–frequency domain to help eliminate light pollution and retain more image information. The experimental results show that on the nighttime light pollution dataset, the Peak Signal-to-Noise Ratio (PSNR) of the model is improved to 24.91 compared to the current state-of-the-art image restoration model, while the Frechet inception distance (FID) is reduced to 64.85. At the same time, in the real night environment, the model can better remove light pollution to recover the original nighttime information. It has excellent performance and provides a certain reference value for advancing the safety of nighttime maritime navigation.

IHR-Net: Image haze removal network based on improved YUV spatial strategy

Abstract Image haze removal is a challenging task in computer vision. Existing dehazing algorithms are typically applied in the RGB space, assuming that haze affects all color channels equally, and thus enhancing the image independently across the three channels. Actually, compared to the RGB space, the YUV domain can provide a more accurate representation of haze distribution characteristics and is better suited for image haze removal. Nevertheless, current YUV-based dehazing strategies often suffer from dull colors due to a lack of comprehensive consideration of color channels. To address this limitation, we propose an improved YUV domain-based image haze removal network (IHR-Net), which consists of a pre-trained model and a backbone network to enhance the Y channel and UV channels separately. Specifically, we utilize the pre-trained model to extract haze features that guide the backbone network in optimizing the UV channels. To increase the network's attention to color-shifting areas, we introduce a haze attention module (HAM) and integrate it into the backbone network. Experimental results on both synthetic and real-world daytime and nighttime images demonstrate the superior performance of IHR-Net.

Estimates of power supply during the 2023 Gaza humanitarian crisis using night-time light images

ABSTRACT Since the outbreak of the Gaza conflict in October 2023, a severe humanitarian crisis has occurred across the entire Gaza Strip. Owing to the challenges of conducting traditional ground-based surveys in the region, satellite images have become critically important as an objective source of information. In response to the demands of humanitarian relief, this study utilized VIIRS, SDGSAT-1, and Yangwang-1 nighttime light (NTL) satellite images to provide a multi-scale assessment of the spatiotemporal dynamics of power supply in the Gaza Strip during the conflict. To reduce errors in quantitatively estimating power supply based on nighttime light, the quality of daily VIIRS NTL images was enhanced and radiometric intercalibrations were conducted between SDGSAT-1 and Yangwang-1 NTL images. The result indicates a severe disruption of power supply in the Gaza Strip as a consequence of the conflict. Specifically, the overall power supply had a sharp decline shortly after the outbreak of the conflict on 7 October 2023 and persisted in a downtrend during the conflict, culminating in a reduction of around 94% as of 1 March 2024. In contrast, the power supply in areas surrounding the Gaza Strip remained relatively stable during the conflict. On 2 December 2023, 24 municipalities were found to have significant reductions in power supply exceeding 90%, and around 95% of buildings had power losses of more than 75%. Furthermore, approximately 70% of hospitals and UNRWA schools in the Gaza Strip were identified to have power outages on 1 January 2024. Among the five governorates of the Gaza Strip, Rafah was the most resilient in terms of power supply during the conflict. This study could make a positive contribution to the timely assessments of the Gaza conflict and support the implementation of humanitarian relief efforts.

FPNIE: a fast pure nighttime image enhancement method

FPNIE: a fast pure nighttime image enhancement method

Research and Application of YOLOv11-Based Object Segmentation in Intelligent Recognition at Construction Sites

With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.

Perceptually-calibrated synergy network for night-time image quality assessment with enhancement booster and knowledge cross-sharing

Image quality assessment (IQA) and image enhancement (IE) of night-time images are highly correlated tasks. On the one hand, IQA task could obtain more complementary information from the enhanced image. On the other hand, IE task would benefit from the prior knowledge of quality-aware attributes. Thus, we propose a Perceptually-calibrated Synergy Network (PCSNet) to simultaneously predict and enhance image quality of night-time images. More specifically, a shared shallow network is applied to extract the shared features for both tasks by leveraging complementary in-formation. The shared features are then fed to task-specific sub-networks to predict quality scores and generate enhanced images in parallel. In order to better exploit the interaction of complementary information, intermediate Cross-Sharing Modules are used to form efficient feature representations for the image quality assessment (IQA) and image enhancement (IE) subnetworks. Experimental results of the night-time image datasets show that the proposed approach achieves state-of-the-art performance on both quality prediction and image enhancement tasks.

An Image Fusion Algorithm for Sustainable Development Goals Satellite-1 Night-Time Light Images Based on Optimized Image Stretching and Dual-Domain Fusion

The Glimmer Imager of Urbanization (GIU) on SDGSAT-1 provides high-resolution and global-coverage images of night-time lights (NLs) with 10 m panchromatic (PAN) and 40 m multispectral (MS) imagery. High-resolution 10 m MS NL images after ideal fusion can be used to better study subtle manifestations of human activities. Most existing remote sensing image-fusion methods are based on the fusion of daytime optical remote sensing images, which do not apply to lossless compressed images of the GIU. To address this limitation, we propose a novel approach for 10 m NL data fusion, namely, a GIU NL image fusion model based on PAN-optimized OIS (OIS) and DDF (DDF) fusion for SDGSAT-1 high-resolution products. The OIS of PAN refers to the optimized stretching method that integrates linear and gamma stretching while DDF indicates a fusion process that separately merges the dark and light regions of NL images using different fusion methods, then stitches them together. In this study, fusion experiments were conducted in four study areas—Beijing, Shanghai, Moscow, and New York—and the proposed method was compared to traditional methods using visual evaluation and five quantitative evaluation metrics. The results demonstrate that the proposed method achieves superior visual quality and outperforms conventional methods across all quantitative metrics. Additionally, the ablation study confirmed the necessity of the methodological steps employed in this study.

Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine learning application

Highly accurate nighttime cloud detection in satellite imagery is challenging due to the absence of visible to near-infrared (0.38–3 μm, VNI) data, which is critical for distinguishing clouds from other ground features. Fortunately, Machine learning (ML) techniques can more effectively leverage the limited wavelength information and show high-accuracy cloud detection based on vast sample volume. However, accurately distinguishing cloud pixels solely through thermal infrared bands (8–14 μm, TIR) is challenging, and acquiring numerous, high-quality and representative samples of nighttime images for ML proves to be unattainable in practice. Given the thermal infrared radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIR, we propose a sample generation idea that uses daytime images to provide samples of nighttime cloud detection, which is different from the traditional sample construction methods (e.g., manually label, simulation and transfer learning method), and can obtain samples effectively. Based on this idea, nighttime cloud detection experiments were carried out for MODIS, GF-5 (02) and Himawari-8 satellites, respectively. The results were validated by the Lidar cloud product and manual labels and show that our nighttime cloud detection result has higher accuracy than MYD35 (78.17%) and the ML model trained by nighttime manual labels (75.86%). The accuracy of the three sensors is 82.19%, 88.71%, and 79.34%, respectively. Moreover, we validated and discussed the performance of our algorithm on various surface types (vegetation, urban, barren and water). The results revealed that the accuracy of the three sensors over barren was found to be poor and varied with surface types but overall high. Our study can provide a novel perspective on nighttime cloud detection of muti-spectral satellite imagery.

UPT-Flow: Multi-scale transformer-guided normalizing flow for low-light image enhancement

Low-light images often suffer from information loss and RGB value degradation due to extremely low or nonuniform lighting conditions. Many existing methods primarily focus on optimizing the appearance distance between the enhanced image and the normal-light image, while neglecting the explicit modeling of information loss regions or incorrect information points in low-light images. To address this, this paper proposes an Unbalanced Points-guided multi-scale Transformer-based conditional normalizing Flow (UPT-Flow) for low-light image enhancement. We design an unbalanced point map prior based on the differences in the proportion of RGB values for each pixel in the image, which is used to modify traditional self-attention and mitigate the negative effects of areas with information distortion in the attention calculation. The Multi-Scale Transformer (MSFormer) is composed of several global-local transformer blocks, which encode rich global contextual information and local fine-grained details for conditional normalizing flow. In the invertible network of flow, we design cross-coupling conditional affine layers based on channel and spatial attention, enhancing the expressive power of a single flow step. Without bells and whistles, extensive experiments on low-light image enhancement, night traffic monitoring enhancement, low-light object detection, and nighttime image segmentation have demonstrated that our proposed method achieves state-of-the-art performance across a variety of real-world scenes. The code and pre-trained models will be available at https://github.com/NJUPT-IPR-XuLintao/UPT-Flow.

Effective video deblurring based on feature-enhanced deep learning network for daytime and nighttime images

Effective video deblurring based on feature-enhanced deep learning network for daytime and nighttime images

Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation

Due to the lack of annotations for nighttime low-light images, object detection in low-light images has always been a challenging problem. Achieving high-precision results at night is also an issue. Additionally, we aim to use a single nighttime dataset to complete the knowledge distillation task while improving the detection accuracy of object detection models under nighttime low-light conditions and reducing the computational cost of the model, especially for small targets and objects contaminated by special nighttime lighting. This paper proposes a Nighttime Unsupervised Domain Adaptation Network (NUDN) based on knowledge distillation to address these issues. To improve the detection accuracy of nighttime images, high-confidence bounding box predictions from the teacher and region proposals from the student are first fused, allowing the teacher to perform better in subsequent training, thus generating a combination of high-confidence and low-confidence pseudo-labels. This combination of feature information is used to guide model training, enabling the model to extract feature information similar to that of source images in nighttime low-light images. Nighttime images and pseudo-labels undergo random size transformations before being used as input for the student, enhancing the model’s generalization across different scales. To address the scarcity of nighttime datasets, we propose a nighttime-specific augmentation pipeline called LightImg. This pipeline enhances nighttime features, transforming them into daytime features and reducing issues such as backlighting, uneven illumination, and dim nighttime light, enabling cross-domain research using existing nighttime datasets. Our experimental results show that NUDN can significantly improve nighttime low-light object detection accuracy on the SHIFT and ExDark datasets. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work.

Nighttime scene understanding with label transfer scene parser

Semantic segmentation plays a crucial role in traffic scene understanding, especially in nighttime conditions. This paper tackles the task of semantic segmentation in nighttime scenes. The largest challenge of this task is the lack of annotated nighttime images to train a deep learning-based scene parser. The existing annotated datasets are abundant in daytime conditions but scarce in nighttime due to the high cost. Thus, we propose a novel Label Transfer Scene Parser (LTSP) framework for nighttime scene semantic segmentation by leveraging daytime annotation transfer. Our framework performs segmentation in the dark without training on real nighttime annotated data. In particular, we propose translating daytime images to nighttime conditions to obtain more data with annotation in an efficient way. In addition, we utilize the pseudo-labels inferred from unlabeled nighttime scenes to further train the scene parser. The novelty of our work is the ability to perform nighttime segmentation via daytime annotated labels and nighttime synthetic versions of the same set of images. The extensive experiments demonstrate the improvement and efficiency of our scene parser over the state-of-the-art methods with a similar semi-supervised approach on the benchmark of Nighttime Driving Test dataset. Notably, our proposed method utilizes only one-tenth of the amount of labeled and unlabeled data in comparison with the previous methods. Code is available at https://github.com/danhntd/Label_Transfer_Scene_Parser.git.

Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement

Most existing low-light image enhancement approaches predominantly focus on directly modeling the mapping between low-light images and normal-light images, but models trained on the single fixed data set often fail to achieve satisfactory results in complex real-world scenes. To address the limitations posed by limited data, this paper proposes a joint framework to learn spatially adaptive degradation and enhancement representations of bright-to-dark images and dark-to-bright images. Firstly, we design an illumination degradation model based on cycle consistent loss to learn how to degrade normal-light images into low-light reference images. The degradation model can synthesize a large number of normal/degraded-light pairs, which is beneficial for the training of the second-stage enhancement model. Subsequently, leveraging the self-luminance information of images, we construct a self-attention-based light enhancement model. The enhancement model is trained on mixed data, which combines both real normal/low-light image pairs and synthetic normal/degraded-light image pairs. In this way, the enhancement model can learn more features from a diverse set of images, enhancing its robustness. Extensive experiments on low-light image enhancement, low-light object detection, and nighttime image segmentation have demonstrated that our proposed method outperforms existing supervised and unsupervised methods across a variety of real-world scenes.

Characterizing the livingness of geographic space across scales using global nighttime light data

The hierarchical structure of geographic or urban space can be well-characterized by the concept of living structure, a term coined by Christopher Alexander. All spaces, regardless of their size, possess certain degrees of livingness that can be mathematically quantified. While previous studies have successfully quantified the livingness of small spaces such as images or artworks, the livingness of geographic space has not yet been characterized in a recursive manner. Zipf’s law has been observed in urban systems and intra-urban structures. However, whether Zipf’s law is applicable to the hierarchical substructures of geographic space has rarely been investigated. In this study, we recursively extract the substructures of geographic space using global nighttime light imagery. We quantify the livingness of global cities considering the number of substructures (S) and their inherent hierarchy (H). We further investigate the scaling properties of the extracted substructures across scales and the relationships between livingness and population for global cities. The results demonstrate that all substructures of global cities form a living structure that conforms to Zipf’s law. The degree of livingness better captures population distribution than nighttime light intensity values for the global cities. This study contributes in three aspects: First, it considers global cities as a whole to quantify spatial livingness. Second, it applies the concept of livingness to cities to better capture the spatial structure of the population using nighttime light data. Third, it introduces a novel method to recursively extract substructures from nighttime images, offering a valuable tool to investigate urban structures across multiple spatial scales.

HNQA: histogram-based descriptors for fast night-time image quality assessment

HNQA: histogram-based descriptors for fast night-time image quality assessment

Dual-branch teacher-student with noise-tolerant learning for domain adaptive nighttime segmentation

While significant progress has been achieved in the field of image semantic segmentation, the majority of research has been primarily concentrated on daytime scenes. Semantic segmentation of nighttime images is equally critical for autonomous driving; however, this task presents greater challenges due to inadequate lighting and difficulties associated with obtaining accurate manual annotations. In this paper, we introduce a novel method called the Dual-Branch Teacher-Student (DBTS) framework for unsupervised nighttime semantic segmentation. Our approach combines domain alignment and knowledge distillation in a mutually reinforcing manner. Firstly, we employ a photometric alignment module to dynamically generate target-like latent images, bridging the appearance gap between the source domain (daytime) and the target domain (nighttime). Secondly, we establish a dual-branch framework, where each branch enhances collaboration between the teacher and student networks. The student network utilizes adversarial learning to align the target domain with another domain (i.e., source or latent domain), while the teacher network generates reliable pseudo-labels by distilling knowledge from the latent domain. Furthermore, recognizing the potential noise present in pseudo-labels, we propose a noise-tolerant learning method to mitigate the risks associated with overreliance on pseudo-labels during domain adaptation. When evaluated on benchmark datasets, the proposed DBTS achieves state-of-the-art performance. Specifically, DBTS, using different backbones, outperforms established baseline models by approximately 25% in mIoU on the Zurich dataset and by over 26% in mIoU on the ACDC dataset, demonstrating the effectiveness of our method in addressing the challenges of domain-adaptive nighttime segmentation.

Nighttime wildlife object detection based on YOLOv8‐night

AbstractMonitoring nocturnal animals in the field is an important task in ecological research and wildlife conservation, but the complexity of nocturnal images and low light conditions make it difficult to cope with traditional image processing methods. To address this problem, researchers have introduced infrared cameras to improve the accuracy of nocturnal animal behaviour observations. Object detection in nighttime images captured by infrared cameras faces several challenges, including low image quality, animal scale variations, occlusion, and pose changes. This study proposes the YOLOv8‐night model, which effectively overcomes these challenges by introducing a channel attention mechanism in YOLOv8. The model is more focused on capturing animal‐related features by dynamically adjusting the channel weights, which improves the saliency of key features and increases the accuracy rate in complex backgrounds. The main contribution of this study is the introduction of the channel attention mechanism into the YOLOv8 framework to create a YOLOv8‐night model suitable for object detection in nighttime images. When tested on nighttime images, the model performs well with a significantly higher mAP (0.854) than YOLOv8 (0.831), and YOLOv8‐night scores 0.856 on mAP_l, which is obviously better than YOLOv8 (0.833) in terms of processing large objects. The study provides a reliable technical tool for ecological research, wildlife conservation and environmental monitoring, and offers new methods and insights for the study of nocturnal animal behaviour.

Jupiter's Hotspots as observed by JIRAM-Juno: limb darkening in thermal infrared

ABSTRACT The Jupiter InfraRed Auroral Mapper (JIRAM) instrument onboard the Juno spacecraft performed repeated observations of Jupiter's North Equatorial Belt (NEB) around the time of 12th Juno pericenter passage on 2018 April 1. The data consist of thermal infrared images and show, among other atmospheric features, two bright Hotspots on the boundary between the NEB and the Equatorial Zone. Night-time images of the same areas at different emission angles were used to constrain the trend of the limb-darkening function. Comparison with simulated observations, computed for different emission angles, total opacities, single scattering albedo ω0, and asymmetry parameter g suggests that ω0 ∼ 0.90 ± 0.05 and g ∼ 0.37 ± 0.15 provide best match with data. Subsequently, we computed the ω0 and g resulting from different size distributions, taking into account the complex refractive indices of ammonium hydrosulfide (NH4SH) by Howett et al. [2007] and Ferraro et al. [1980]. Only the former data set is marginally consistent with JIRAM observations. Similarly, ammonia and hydrazine barely reproduce the experimental data. Tholin, although not usually considered a realistic component for Jupiter's aerosols, provides a better match for particle radii between 0.7 and 1 μm, both as a pure material as well as a thick coating over NH4SH cores. Notably, this radius range is consistent with the mean radius of aerosols as estimated by Ragent et al. [1998] on the basis of Galileo entry probe data. Comparison with literature suggests that similar results can be achieved by a large variety of contaminants bearing C–N bounds.

Learning sparse feature representation for blind quality assessment of night-time images

Capturing Night-Time Images (NTIs) with high-quality is quite challenging for consumer photography and several practical applications. Thus, addressing the quality assessment of night-time images is urgently needed. Since there is no available reference image for such images, Night-Time image Quality Assessment (NTQA) should be done blindly. Although Blind natural Image Quality Assessment (BIQA) has attracted a great deal of attention for a long time, very little work has been done in the field of NTQA. Due to the capturing conditions, NTIs suffer from various complex authentic distortions that make it a challenging field of research. Therefore, previous BIQA methods, do not provide sufficient correlation with subjective scores in the case of NTIs and special methods of NTQA should be developed. In this paper we conduct an unsupervised feature learning method for blind quality assessment of night-time images. The features are the sparse representation over the data-adaptive dictionaries learned on the image exposure and gradient magnitude maps. Having these features, an ensemble regression model trained using least squares gradient boosting scheme predicts high correlated objective scores on the standard datasets.

A flare removal network for night vision perception: Resistant to the interference of complex light

AbstractThe high‐precision visual perception results are easily affected by the lens flare issue when the image sensor is facing to strong light. The existing flare removal methods have poor robustness when confronted with flare interference caused by complex nighttime lighting, which has to preserve natural light source information. A simulated dataset for the removal of night flares is created to solve the problem of collecting complete paired training data, and night flare removal network (NFR‐Net) is proposed to remove the interference caused by various light disturbances at night. The light source extraction module is introduced to retain light source information realistically and effectively in night vision scenes. Extensive experimental results demonstrate that the proposed method is superior to the existing related methods in the various complex night vision scenes. The proposed NFR‐Net can enhance visual perception of nighttime images significantly and improve the performance of night vision tasks.