Harsh Light Research Articles

Exploring Fast and Flexible Zero‐Shot Low‐Light Image/Video Enhancement

AbstractLow‐light image/video enhancement is a challenging task when images or video are captured under harsh lighting conditions. Existing methods mostly formulate this task as an image‐to‐image conversion task via supervised or unsupervised learning. However, such conversion methods require an extremely large amount of data for training, whether paired or unpaired. In addition, these methods are restricted to specific training data, making it difficult for the trained model to enhance other types of images or video. In this paper, we explore a novel, fast and flexible, zero‐shot, low‐light image or video enhancement framework. Without relying on prior training or relationships among neighboring frames, we are committed to estimating the illumination of the input image/frame by a well‐designed network. The proposed zero‐shot, low‐light image/video enhancement architecture includes illumination estimation and residual correction modules. The network architecture is very concise and does not require any paired or unpaired data during training, which allows low‐light enhancement to be performed with several simple iterations. Despite its simplicity, we show that the method is fast and generalizes well to diverse lighting conditions. Many experiments on various images and videos qualitatively and quantitatively demonstrate the advantages of our method over state‐of‐the‐art methods.

Infrared and Harsh Light Visible Image Fusion Using an Environmental Light Perception Network.

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions.

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Deep Learning Approach for automatic detection of split defects on sheet metal stamping parts

Sheet metal stamping processes are used primarily for high-volume products produced for a range of sectors, from white goods manufacturing to the automotive and aerospace sectors. However, the process is susceptible to defects. Due to the numerous potential defects that may arise in the stamping product, human inspectors are often deployed for their detection. However, they are unreliable and expensive, especially when operating at production speeds equivalent to the stamping rate. This study investigate CNN-based automatic inspection for stamping defects. The study carried out two sets of experiments. All the Experiments yielded high classification accuracy, recall and precision demonstrating the viability of the CNN method for defect detection in the sheet metal stamping process. Additionally, this study revealed that in limited data confounding factors can be a challenge. The second experiment further explored the impact of small neck defects, harsh lighting and reflections on defect detection. The observations indicated that the model struggled to identify defects occluded by reflections, particularly small neck defects.

Cellulose-based light-management film exhibiting flame-retardant and thermal-healing properties

The increased use and expansion of biomass applications offer a viable approach to diminish reliance on petroleum-derived resources and promote carbon neutrality. Cellulose, being the most abundant natural polymer on Earth, has garnered considerable attention. This study introduces a straightforward method to fabricate a cellulose-based multifunctional composite film designed for efficient light management, specifically featuring flame retardant and thermal-healing capabilities. The film incorporates a microfibrillated cellulose (MFC) matrix with functional components, namely benzoxazine resin (BR) and 2-hydroxyethyl methacrylate phosphate (HEMAP). Utilizing dynamic covalent crosslinking, the composite films exhibit satisfactory self-healing properties. The combined effects of BR and HEMAP contribute to the effective flame retardancy of the composite film. Furthermore, the resulting film shields ultraviolet and blue light, offering comfortable interior lighting by mitigating harsh light and extending light propagation. The film also demonstrates favorable water resistance and high tensile strength. The exceptional multifunctional properties, coupled with its safety and extended service life, position it as a potential optical management film for smart building materials.

Event encryption: rethinking privacy exposure for neuromorphic imaging

Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range. Such devices often serve in surveillance systems due to their applicability and robustness in environments with high dynamics and harsh lighting, where they can still supply clearer recordings than traditional imaging. In other words, when it comes to privacy-relevant cases, neuromorphic cameras also expose more sensitive data and pose serious security threats. Therefore, asynchronous event streams necessitate careful encryption before transmission and usage. This work discusses several potential attack scenarios and approaches event encryption from the perspective of neuromorphic noise removal, in which we inversely introduce well-crafted noise into raw events until they are obfuscated. Our evaluations show that the encrypted events can effectively protect information from attacks of low-level visual reconstruction and high-level neuromorphic reasoning, and thus feature dependable privacy-preserving competence. The proposed solution gives impetus to the security of event data and paves the way to a highly encrypted technique for privacy-protective neuromorphic imaging.

Spoke, liefde en geslagsgebaseerde geweld in "Die bouval op Wilgerdal"

The portrayal of gender-based violence is present in all genres of prose, including the ghost story. There is currently a lack of research regarding gender-based violence portrayed in Afrikaans ghost stories in literature as well as in film and television. Alongside the acknowledged forms of gender-based violence (physical and non-physical abuse), I argue that an additional level of violence, namely psychic violence, is used in the ghost story to amplify the horror and impact of gender-based violence. To illustrate this psychic form of violence, I compare the portrayal of the character Emmie in the classic Afrikaans ghost story "Die bouval op Wilgerdal" (The Wilgerdal Ruins) by C. J. Langenhoven (1924) with her portrayal in the 2019 adaptation of Langenhoven's short story for two episodes of the television series Die Spreeus (The Starlings). Emmie is haunted by both the ghost of her deceased beloved, Petrus, and the ghost of her stalker, Frans, who possessed Petrus's body at the moment of his death. Where Emmie was moved to the background to deal with her trauma in silence and solitude in Langenhoven's short story, she is put front and centre in Die Spreeus when her strange situation is investigated by the police as a case of domestic violence. The depiction of Emmie and this investigation casts a harsh light on the continuous plight of female victims of domestic violence, including revictimization, ongoing physical and emotional violence, victim blaming and gaslighting.

Hitler: Rewriting a historical figure in Éric-Emmanuel Schmitt's La Part de l'autre

The aim of this study is to stylise a historical figure that has become universal: Hitler. Schmitt works as a novelist, drawing on a known referentiality to trace the causes of the Führer's actions, albeit subjectively. Through fiction, he shines a harsh light on the character's psychology, the better to de-heroise him and, by means of a "reductio ad hitlerum", reflect back to him the opprobrium he has cast in the face of the entire human race. Using a variety of techniques, the author sets out to deconstruct Hitler and tell a plausible story. Keywords: Stylisation, referentiality, deconstruction, de-heroisation, reductio ad hitlerum

Mechanism of a novel antibacterial polymeric film with freshness‐retentive and hygiene‐keeping functions

AbstractThe growing concerns surrounding food loss and waste, coupled with the amplified need for effective antimicrobial technologies due to the COVID‐19 pandemic have highlighted the significance of antimicrobial solutions. This study introduces novel polymer‐based antibacterial films to address such challenges by combining antibacterial properties with durability. Using stearyldiethanolamine (C18DEA) as the active ingredient, the polyethylene‐based (PE) film is designed to prevent bacterial growth on its surface. The present study investigated the antibacterial mechanism, durability, and effectiveness of the films against representative gram‐positive and gram‐negative bacterial strains. The films developed in this study demonstrated notable durability against high water temperatures and harsh light exposure for preserving its antibacterial function on the tested bacteria from both representative groups. Scanning electron microscopy (SEM) analysis of bacteria in contact with film surface revealed damages to cellular structure leading to cell lysis even at the lower tested concentration of 800 ppm C18DEA in the film. Our proposed bactericidal mechanism suggests the alkyl chain of C18DEA disrupts bacterial cell membranes, leading to irreversible damage and cell death. Overall, the films hold significant promise for diverse applications, including extended shelf life for perishable foods and enhanced hygiene management, driven by their durability and potent antimicrobial effects.Highlights Mechanism of action of a PE film with C18DEA as active ingredient was studied. Broad‐spectrum bactericidal effect on gram‐positive and gram‐negative bacteria. Films demonstrated resistance to high water temperatures and light exposure. Study highlights the films' application in hygiene, safety, and food preservation.

Improved Retinex algorithm for low illumination image enhancement in the chemical plant area

Due to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.

Flow-Based 6D Pose Tracking of Uncooperative Spacecrafts

In this work, an optical-flow-based pose tracking method with long short-term memory for known uncooperative spacecraft is proposed. In combination with the segmentation network, we constrain the optical flow area of the target to cope with harsh lighting conditions and highly textured background. With the introduction of long short-term memory structure, the proposed method can maintain a robust and accurate tracking performance even in a long-term sequence of images. In our experiments, the pose tracking effects in the synthetic images as well as the SwissCube dataset images are tested, respectively. By comparing with the state-of-the-art pose tracking frameworks, we demonstrate the performance of our method and in particular the improvements under complex environments.

10.M. Workshop: Community Health Practice and Research in Europe: Up front and centre!

Abstract Community Health (CH) is one of the most undervalued and poorly funded domains of action of health systems. Although evidence is strong on its contribution to population health, well-being and equity, national and regional health systems, and to some extent public health institutions, tend to pay little attention to its contribution. Community health encompasses the application of health promotion principles and strategies at the local level to improve equity and resilience of a community. It does so through three fundamental means: cross-sectoral collaborations, community involvement/engagement, and community health workers (CHW). It engages and works with communities in a culturally appropriate manner in order to implement effective strategies. In community health, capacity building is essential. The aim of this workshop is three-fold: 1) to generate a discussion among participants on the state of practice and research, 2) to reflect on strategies to strengthen and improve community health in Europe and, 3) to enhance the visibility of this domain within the ranks of the European Public Health Association (EUPHA). The workshop will provide a space for reflections and discussions. Each of our speakers, who are researchers from 5 different countries (Belgium, Czech Republic, Germany, Spain, and UK), will have 5 minutes to provide some elements of answers to the two following questions: • What is the state of community health practice and research in your country? • What difference does it make for health systems to implement community health strategies? Eric Breton (France) will briefly introduce the workshop providing a conceptual framework for community health and an overview of the different perspectives that drive practices and policies. Manfred Cassens (Germany) and Caroline Masquillier (Belgium) will discuss how the COVID-19 pandemic has enhanced the shortfalls of health systems and lead their countries to experiment community health strategies. Camila Picchio (Spain) will demonstrate how CHWs are essential in reaching out marginalised communities as will Ekaterina Volevach (Czech Republic) drawing lessons from a programme targeting the Roma population. Both will highlight the difficulties health systems face in answering the need of vulnerable population. Catherine Jenkins (United Kingdom), will describe a city-region intervention for galvanising community-level innovation to reduce gambling-related harms. Luis Saboga-Nunes, President of the EUPHA Health Promotion Section and Goof Buijs, Manager of the UNESCO Chair Global Health & Education will conclude the session. We trust that this very interactive workshop will help attendees of the EUPHA conference in Dublin working in community health to find each other and facilitate the development of partnerships. Key messages • Community Health strategies are essential in improving populations’ health and well-being. Yet, this contribution is hardly reflected in national health policies and in the resources invested locally. • The COVID-19 pandemic cast a harsh light on the limits of health systems under-investing in community engagement, community health workers and in the mobilisation of local stakeholders.

Illuminating the augmented reality stage: Assessment of lighting conditions for space exploration

Augmented reality (AR) technology faces a prevailing challenge when dealing with harsh lighting environments, which are common in space. The study aims to evaluate the lighting compatibility for successful hand gestures using HoloLens 2, a device that utilizes AR technology for heads-up displays (HUDs), as a potential way to provide critical information to astronauts during extravehicular activities (EVAs). The study modulated luminance (dim, neutral, bright) and hand coverings (bare hand, white glove, black glove) to determine the HoloLens functionality. The results will inform design changes to support the astronaut experience by enhancing hand gesture recognition and reducing time wasted re-gesturing. By understanding the lighting requirements for HoloLens, we can improve the AR experience for future space missions. This study highlights the importance of considering lighting conditions in the development of AR technology, especially in challenging environments like space.

A Zero-Shot Low Light Image Enhancement Method Integrating Gating Mechanism.

Photographs taken under harsh ambient lighting can suffer from a number of image quality degradation phenomena due to insufficient exposure. These include reduced brightness, loss of transfer information, noise, and color distortion. In order to solve the above problems, researchers have proposed many deep learning-based methods to improve the illumination of images. However, most existing methods face the problem of difficulty in obtaining paired training data. In this context, a zero-reference image enhancement network for low light conditions is proposed in this paper. First, the improved Encoder-Decoder structure is used to extract image features to generate feature maps and generate the parameter matrix of the enhancement factor from the feature maps. Then, the enhancement curve is constructed using the parameter matrix. The image is iteratively enhanced using the enhancement curve and the enhancement parameters. Second, the unsupervised algorithm needs to design an image non-reference loss function in training. Four non-reference loss functions are introduced to train the parameter estimation network. Experiments on several datasets with only low-light images show that the proposed network has improved performance compared with other methods in NIQE, PIQE, and BRISQUE non-reference evaluation index, and ablation experiments are carried out for key parts, which proves the effectiveness of this method. At the same time, the performance data of the method on PC devices and mobile devices are investigated, and the experimental analysis is given. This proves the feasibility of the method in this paper in practical application.

Unmasking Structural Racism in U.S. Medical Education: Advancing Equity for Underrepresented Medical Students

The COVID pandemic cast a harsh light on the structural and systemic health inequalities that exist in American society and in U.S. medical education. Black and Brown communities were disproportionately affected, and the pandemic highlighted the need for a diverse physician and healthcare workforce. Both the lack of equitable, high-quality healthcare in underrepresented communities and the obstacles that students who are underrepresented in medicine (URiM) experience in medical school are direct consequences of the structural racism that flourishes in U.S. medical schools and healthcare institutions. In this article, we explain structural racism and how it has manifested itself in medical education, including the lack of diversity among faculty and leadership, implicit biases and stereotypes about people of color, and discriminatory language used in evaluations of URiM students. We conclude with potential solutions for addressing structural racism in medical education. These include increasing diversity among faculty and leadership, implementing antiracist curricula, and providing mentorship and support for URiM students. Ultimately, we aim to promote discussion and action to eliminate structural racism in medical education in America.

We Need to Talk about Class

Abstract Class differences have historically received limited attention in museum theory and practice, and scholarly publications on issues of class and heritage are still scarce. COVID-19 has shone a particularly harsh light on class divisions. At the height of the pandemic, working-class laborers (such as supermarket cashiers, social care workers, truck and delivery drivers) were asked to shoulder high levels of health risks, exposing entrenched socioeconomic inequities. In this article, we build upon a small-scale research and collecting project, Inequalities, Class, and the Pandemic, carried out in 2021 by the London Museum (formerly known as the Museum of London) and King's College London, to discuss how museums can meaningfully engage with working-class lived experiences in our contemporary neo-liberal societies. We begin by analyzing whether and how museums have addressed working-class issues and (hi)stories. We then draw on the voices and experiences of our research participants to examine the ongoing structural inequalities experienced by working-class Londoners. Building on our empirical research, we argue for museums to play an active role in reclaiming the centrality of class in public culture, particularly addressing the contemporary lived experiences of working-class people.

Achieving a green university in the post pandemic era: Edo State University Uzairue experience

The complexity and dynamicity of our world calls for institutions to be more creative and research-driven to surmount global challenges in the education system. Institutions now need to be innovative and creative to be successful, but while the idea of success has changed, the education system especially in Africa has not always adjusted to its methods or goals to meet the ever changing world. In the pre COVID-19 era, many tertiary institutions within Africa did not perceive the need to adopt and invest in ICT education for teaching, learning and research. This unprecedented situation has shined a harsh light on the vulnerabilities, challenges, weaknesses of the Education system and our unpreparedness for a paradigm shift from the conventional teaching classroom methods to digital and then distance learning. In recent times, the campaign for adoption of sustainable practices within universities has been critically championed to prevent future occurrences of education disruption. In the post pandemic era, African universities should encourage the utilization of ICT technology to transform teaching and learning in order to improve on research and education to prepare students for unexpected occurrences like the COVID-19 pandemic, acquire settings and infrastructure to enhance teaching and learning, adopt a proper waste management policy to reduce climate change and promote all round sustainability as well as energy conservation. This work presents the institutional values and activities of Edo State University Uzairue (EDSU), Nigeria during the pre and post-covid 19 era with a strong understanding of the role of sustainability in achieving our goal. EDSU success story was also explored and presented in line with the role of ICT and learning management system for resource optimization and development of a green system through the adoption of energy conservation protocols, developing waste management policies, conservation of biodiversity and aligning with the global net zero emission initiative.

A Safety Goggles for Dental Treatment That Can Monitor the Pulse

Objective: Using the safety goggles to protect and treat the process of fog, powder or laser injury to the eyes, reduce noise and thus reduce the patient's discomfort. At the same time, pulse frequency and vital signs of patients can be detected during treatment to improve the safety of treatment. Methods: The device is a safety goggles for dental treatment that can monitor the pulse, including a mirror frame, a protective lens mounted on one side of the frame and a first elastic band and a second elastic band located at both ends of the frame. The first elastic band and the second elastic band are connected and fixed together. The first elastic band is provided with a holding box body near the frame, and the top of the holding box body is provided with a power switch, an audio switch and a pulse monitoring switch. The body of the containing box is provided with a control chip, a power supply component, a pulse sensor, a memory and an audio component. The control chip is electrically connected with the memory, a pulse sensor, an audio component, a speaker and a power supply component, and the audio component is electrically connected with the first earplug and the second earplug through the data cable. Results: A kind of safety goggles for dental treatment which can monitor pulse was successfully designed. The device is equipped with safety goggles, so as to prevent the air fog and other dirt damage to the eyes; Pulse sensor was set to obtain pulse signal; Earplugs with extendable length are suitable for individual differences and can improve the sound insulation effect. Conclusion: The safety goggles can solve the problems of harsh light source equipped with dental treatment chair, easy to splash the eyes with aerosol during operation, and loud noise of operating instruments. In addition, it can also monitor patients' vital signs in real time during tooth implantation and extraction and prevent sudden symptoms.

Photoexcited Nitroarenes as Anaerobic Oxygen Atom Transfer ­Reagents

AbstractApplications of photoexcited nitroarenes have been underdeveloped in organic synthesis. Since early reports on the direct excitation of nitroaromatics with harsh UV light, these synthetically useful reagents have not been tamed for use in modern synthetic chemistry. We have developed practical synthetic protocols for the anaerobic oxidation of hydrocarbon substrates using commercially available nitroarenes as photochemically activated oxidants under visible light. Using this approach, a wide variety of olefins are anaerobically cleaved to their corresponding carbonyls, and aliphatic C–H bonds are hydroxylated to give alcohols. The anaerobic reaction conditions enable oxidatively sensitive functional groups to be tolerated and the employment of visible light makes this method highly sustainable. Mechanistic studies support that the photoexcited nitroarene biradical intermediate is responsible for the oxygen atom transfer events.1 Introduction2 Alkene Cleavage Promoted by Photoexcited Nitroarenes3 Photoinduced Nitroarene-Mediated C–H Hydroxylation4 Conclusions

Documenting Outdoor Simulated Scenes with Photogrammetry

In both traditional and forensic archaeology, the thorough documentation of context is essential to a proper excavation or recovery, as the processing of archaeological sites and forensic scenes is an inherently destructive process. Over the past few decades, close-range photogrammetry (CRP) has become increasingly utilized by archaeologists to digitally record their excavations. However, CRP has not been employed regularly during outdoor forensic archaeological scene recoveries, and therefore protocols are not well defined for this context. One important consideration when employing CRP is the impact of natural lighting conditions. Light is an important variable when recording scenes using CRP because the quality of light can affect the visual quality of the model and imagery. In particular, shadows from the photographer’s body, trees, and other structures are unavoidable when collecting images around the perimeter of scenes, a requirement of photogrammetry data collection. Therefore, the purpose of the present study was to determine whether implementing light correction tools would improve the three-dimensional model imagery. Two mock forensic scenes with different lighting conditions were photographed four times each in order to test the application of light correction tools, including a large tarp and artificial lights, for mitigating harsh lighting conditions. The results demonstrate that a tarp is a viable light correction tool that can improve the visual quality of the final models by eliminating lighting inconsistencies in both open and wooded environments. Based on these results, improved guidelines for the application of CRP to outdoor forensic archaeological scenes are presented.