Research progress of true color digital night vision technology

Ren, Bin; Jiao, Gangcheng; Li, Ye; Zhou, Zheng; Qiao, Kai; Yang, Yuesuo; Yan, Lei; Yuan, Yuan

doi:10.1117/12.2587330

Cited by 5 publications

(4 citation statements)

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“…This paper processes the low light level image obtained from the low light level night vision instrument to further verify the universality of the method proposed in this paper [30] . The results are as shown in Figs.…”

Section: Light Level Night Vision Image Enhancementmentioning

confidence: 93%

Low-light image enhancement based on Double component processing of Retinex model

Chen

Liu

Deng

et al. 2022

Preprint

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The Retinex model has been shown to be an effective method of enhancing shimmering images, and how to deal with the illuminance and reflection components decomposed from Retinex model is the key to obtain high-quality enhanced images.To better apply the Retinex model to achieve more effective micro-optical image enhancement, this paper proposes an image enhancement method based on Retinex model with dual enhancement of the illuminance and reflection components.First, to reduce the computational complexity of the algorithm, the original RGB image is converted to HSV color space, and the V component is extracted for subsequent processing in this paper; Then,processing V components by guided filter to obtain the illuminance component according to Retinex model,so that the obtained illuminance component will not have edge loss;The reflection component is then obtained by logarithmic decomposition.Then global adaptive brightness enhancement and local contrast enhancement are performed for illuminance components; detail enhancement was applied to the reflection component.Finally the enhanced illuminance and reflection components are combined into an enhanced image. Experiments on a large number of datasets show that this method can achieve better image enhancement in both subjective and objective evaluation, and is superior to or similar to the existing methods.

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Section: Light Level Night Vision Image Enhancementmentioning

confidence: 93%

Low-light image enhancement based on Double component processing of Retinex model

Chen

Liu

Deng

et al. 2022

Preprint

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“…Any color that the naked eye can see in nature can be mixed and superimposed from the optical three-primary colors, which refer to red (R), green (G), and blue (B). By analyzing the RGB values of the image, the concentration of the target substance can be quantitatively analyzed [134,135].…”

Section: Optical Three-primary-color (Rgb) Methodsmentioning

confidence: 99%

Advances in Non-Electrochemical Sensing of Human Sweat Biomarkers: From Sweat Sampling to Signal Reading

Yang,

Sun,

Lai

et al. 2023

Biosensors

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Sweat, commonly referred to as the ultrafiltrate of blood plasma, is an essential physiological fluid in the human body. It contains a wide range of metabolites, electrolytes, and other biologically significant markers that are closely linked to human health. Compared to other bodily fluids, such as blood, sweat offers distinct advantages in terms of ease of collection and non-invasive detection. In recent years, considerable attention has been focused on wearable sweat sensors due to their potential for continuous monitoring of biomarkers. Electrochemical methods have been extensively used for in situ sweat biomarker analysis, as thoroughly reviewed by various researchers. This comprehensive review aims to provide an overview of recent advances in non-electrochemical methods for analyzing sweat, including colorimetric methods, fluorescence techniques, surface-enhanced Raman spectroscopy, and more. The review covers multiple aspects of non-electrochemical sweat analysis, encompassing sweat sampling methodologies, detection techniques, signal processing, and diverse applications. Furthermore, it highlights the current bottlenecks and challenges faced by non-electrochemical sensors, such as limitations and interference issues. Finally, the review concludes by offering insights into the prospects for non-electrochemical sensing technologies. By providing a valuable reference and inspiring researchers engaged in the field of sweat sensor development, this paper aspires to foster the creation of innovative and practical advancements in this domain.

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“…The element that was not tested during this study was when the driver or passenger dressed in black, but believed it is sufficient because the seat belt creates a sufficient difference because it has the same width and thickness throughout. Technically, there are cameras with night vision or full color that have produced the same results today even if there was no lighting present during the capturing of the images, so even with this we can avoid fraud, where alternative methods and techniques, like NVD, can be applied as [33].These works can also be used in various analyses such as [34] through deep learning, which has discovered how many drivers have not worn their seat belts based on images, as well as some other analyses such as edge detection, the table of registration, plan the path, fuzzy logic, piezo sensors [35] [36] [37] [38]. As a result, they concluded that using these forms can lead to more efficient and automatic monitoring.…”

Section: Prediction = Svm_minscore(input_data) # Run ML Modelmentioning

confidence: 99%

Innovative Approaches for Automated Seat Belt Compliance Detection

2023

jjmie

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This research delves into the significance of seat belt utilization, particularly within public transportation, for both drivers and passengers. The article examines the issue of non-compliance with seat belt regulations, including the use of counterfeit manufacturer items to disable seat belt warnings and improper seat belt positioning. With a focus on advanced technology, the article proposes various technical approaches to enhance the automatic detection of seat belt usage. These suggested methods entail the integration of a camera and buzzer with a microcontroller of seat belt compliance detection and the development of a supervised machine learning model that achieved a 99% accuracy rate in identifying seat belt usage through image analysis. Furthermore, the research introduces an innovative approach to verifying individuals' specific health conditions that health authorities have granted limited exemptions from wearing seat belts. The ultimate objective of this study is to promote seat belt usage, thereby safeguarding both drivers and passengers. It is intended that the findings of this research be considered by the vehicle manufacturing industry.

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Research progress of true color digital night vision technology

Cited by 5 publications

References 0 publications

Low-light image enhancement based on Double component processing of Retinex model

Low-light image enhancement based on Double component processing of Retinex model

Advances in Non-Electrochemical Sensing of Human Sweat Biomarkers: From Sweat Sampling to Signal Reading

Innovative Approaches for Automated Seat Belt Compliance Detection

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