• Journal of Bio-Environment Control
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• v.12 no.1
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• pp.17-21
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• 2003

This experiment was conducted to find out the method of preventing decrease in the marketable yield of oriental melon (Cucumis melo L. var. makuwa MAKINO) under low light intensity. By maintaining low light of 400 $\mu$mol$.$m$^{[-10]}$ 2$.$S$^{-1}$ from 10 days after fruit set to fruit enlargement period, the photosynthetic rate and chlorophyll contents of leaf were reduced. Leaves which had no urea application showed largely decreased photosynthetic rate The content of soluble solids was lower$.$ in the low light than natural light treatment. Regardless of foliar application of urea, % fermentation fruits was under 4% in the natural light treatment and over 10% in the low light treatment. The less the fruit thinning, the greater was % fermentation fruits under low light condition. The % fermentation fruits were 39% and 40% in no fruit thinning treatment. The harvest was delayed under low light condition regardless of foliar fertilization. As the number of thinned fruits was decreased, the harvest time was delayed more. Marketable yield per plant sharply decreased under low light intensity. Compared with natural light, the yield under low light treatment was 16∼34%. The treatment fertilized with 0.5% urea on leaf had 34% greater harvest index of marketable yield than other treatments. In conclusion, when the long low light condition from 10th day after fruiting was forecasted, thinning two fruits out of six fruits and two times foliar fertilization with 0.5% urea should be applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of Sensor Science and Technology
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• v.29 no.3
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• pp.180-186
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• 2020

In this study, we applied edge-pulse modulation to prevent the flicker of light-emitting diode (LED) light in a visible light identification system. In the visible light transmitter, positive pulses were transmitted at the edges of the low-to-high transition points, and negative pulses were transmitted at the edges of the high-to-low transition points of the non-return-to-zero (NRZ) data waveforms. In the visible light receiver, the NRZ waveforms were regenerated by making low-to-high and high-to-low transitions at the point of the positive and negative pulses, respectively. This method has two advantages. First, it ensures that the LED light is flicker-free because the average optical power of the LED was kept constant during data transmission in the transmitter. Second, the 120 Hz optical noise from the adjacent lighting lamps was easily cut off using a simple RC-high pass filter in the receiver.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.905-908
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• 2006

In this study examines physical and mechanical properties the use of domestic low grade meta-kaolin in light weight porous concrete. For this purpose light weight porous concrete incorporating low grade meta-kaolin admixture, was tested for tensile strength and acoustic characteristics. Checking tensile strength of cement and low grade meta-kaolin mixture was used to determine the optimum mix proportion of the low grade meta-kaolin admixture. In this paper sound absorbing material has been investigated by using the light weight porous concrete.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2334-2342
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• 2015

This paper proposes an LED driving circuit with a digital controller, power factor correct (PFC) function, and low light flicker. The key topology of the proposed circuit is a conventional Flyback combined with a pre-stage. As a result, there will be less light flicker than with other one-stage PFC circuits. A digital controller, implemented using a low-cost microcontroller, dsPIC30F2020, will meet PFC and low light flicker. The experimental results validate the functionality of the proposed circuit.

• Journal of IKEEE
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• v.22 no.3
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• pp.585-589
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• 2018

Consumer demand for high quality image/video services led to growing trend in image quality enhancement study. Therefore, recent years was a period of substantial progress in this research field. Through careful observation of the image quality after processing by image enhancement algorithms, we perceived that the dark region in the image usually suffered loss of contrast to a certain extent. In this paper, the low-light stretch preprocessing algorithm is, hence, proposed to resolve the aforementioned issue. The proposed approach is evaluated qualitatively and quantitatively against the well-known histogram equalization and Photoshop curve adjustment. The evaluation results validate the efficiency and superiority of the low-light stretch over the benchmarking methods. In addition, we also propose the 255MHz-capable hardware implementation to ease the process of incorporating low-light stretch into real-time imaging systems, such as aerial surveillance and monitoring with drones and driving aiding systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.5
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• pp.243-250
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• 2024

This study explores enhancing facial recognition performance in low-light environments using deep learning-based low-light enhancement techniques. Facial recognition technology is widely used in edge devices like smartphones, smart home devices, and security systems, but low-light conditions reduce accuracy due to degraded image quality and increased noise. We reviewed the latest techniques, including Zero-DCE, Zero-DCE++, and SCI (Self-Calibrated Illumination), and applied them as preprocessing steps in facial recognition on edge devices. Using the K-face dataset, experiments on the Qualcomm QRB5165 platform showed significant improvements in F1 SCORE from 0.57 to 0.833 with SCI. Processing times were 0.15ms for SCI, 0.4ms for Zero-DCE, and 0.7ms for Zero-DCE++, all much shorter than the facial recognition model MobileFaceNet's 5ms. These results indicate that these techniques can be effectively used in resource-limited edge devices, enhancing facial recognition in low-light conditions for various applications.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.135-145
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• 2020

Recently, deep-learning based methods for low-light image enhancement accomplish great success through supervised learning. However, they still suffer from the lack of sufficient training data due to difficulty of obtaining a large amount of low-/normal-light image pairs in real environments. In this paper, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP), which gives the constraint that the brightest pixel in a small patch is likely to be close to 1. With this prior, pseudo ground-truth is first generated to establish an unsupervised loss function. The proposed enhancement network is then trained using the proposed unsupervised loss function. To the best of our knowledge, this is the first attempt that performs a low-light image enhancement through unsupervised learning. In addition, we introduce a self-attention map for preserving image details and naturalness in the enhanced result. We validate the proposed method on various public datasets, demonstrating that our method achieves competitive performance over state-of-the-arts.