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Journal articles on the topic 'LIGHT ENHANCEMENT'

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Author: Grafiati

Published: 11 September 2023

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1

Ono, Naoki, and Kiichi Urahama. "Enhancement of Images Degraded by Hazy Light Scattering and Attenuation." Journal of the Institute of Industrial Applications Engineers 7, no. 2 (April 25, 2019): 38–41. http://dx.doi.org/10.12792/jiiae.7.38.

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2

SANTHIYA, S., S. NANDHINI, M. MOGANA PRIYA, and K. SELVA BHUVANESWARI. "LOW-LIGHT IMAGE ENHANCEMENT USING INVERTED ATMOSPHERIC LIGHT." i-manager’s Journal on Software Engineering 15, no. 4 (2021): 8. http://dx.doi.org/10.26634/jse.15.4.18142.

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3

Zhmakin, A. I. "Enhancement of light extraction from light emitting diodes." Physics Reports 498, no. 4-5 (February 2011): 189–241. http://dx.doi.org/10.1016/j.physrep.2010.11.001.

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4

Hao, Shijie, Xu Han, Yanrong Guo, and Meng Wang. "Decoupled Low-Light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (November 30, 2022): 1–19. http://dx.doi.org/10.1145/3498341.

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The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion, and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to sub-optimal results. In this article, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improving the appearance fidelity by suppressing the rest degeneration factors. The decoupled model facilitates the enhancement in two aspects. On the one hand, the whole low-light enhancement can be divided into two easier subtasks. The first one only aims to enhance the visibility. It also helps to bridge the large intensity gap between the low-light and normal-light images. In this way, the second subtask can be described as the local appearance adjustment. On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information. In the experiments, our model demonstrates the state-of-the-art performance in both qualitative and quantitative comparisons, compared with other low-light image enhancement models. In addition, the ablation studies also validate the effectiveness of our model in multiple aspects, such as model structure and loss function.

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5

Hsieh, Hsin-Hsin, Jen-Loong Hwang, Chia-Yu Lin, and Jang-Hsing Hsieh. "Light Enhancement of Solar Module." Energy and Power Engineering 06, no. 14 (2014): 507–12. http://dx.doi.org/10.4236/epe.2014.614044.

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6

Sukmanowski, J., J. R. Viguié, B. Nölting, and F. X. Royer. "Light absorption enhancement by nanoparticles." Journal of Applied Physics 97, no. 10 (May 15, 2005): 104332. http://dx.doi.org/10.1063/1.1899249.

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7

Park, Seonhee, Kiyeon Kim, Soohwan Yu, and Joonki Paik. "Contrast Enhancement for Low-light Image Enhancement: A Survey." IEIE Transactions on Smart Processing & Computing 7, no. 1 (February 28, 2018): 36–48. http://dx.doi.org/10.5573/ieiespc.2018.7.1.036.

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8

KOJIMA, Seiichi, Noriaki SUETAKE, and Eiji UCHINO. "A Contrast Enhancement of Low-light Image Suppressing Over-enhancement." Japanese Journal of Ergonomics 56, Supplement (2020): 2B3–03–2B3–03. http://dx.doi.org/10.5100/jje.56.2b3-03.

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9

Gebek, Andrea, and Jorryt Matthee. "On the Variation in Stellar α-enhancements of Star-forming Galaxies in the EAGLE Simulation." Astrophysical Journal 924, no. 2 (January 1, 2022): 73. http://dx.doi.org/10.3847/1538-4357/ac350b.

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Abstract The ratio of α-elements to iron in galaxies holds valuable information about the star formation history (SFH) since their enrichment occurs on different timescales. The fossil record of stars in galaxies has mostly been excavated for passive galaxies, since the light of star-forming galaxies is dominated by young stars, which have much weaker atmospheric absorption features. Here we use the largest reference cosmological simulation of the EAGLE project to investigate the origin of variations in stellar α-enhancement among star-forming galaxies at z = 0, and their impact on integrated spectra. The definition of α-enhancement in a composite stellar population is ambiguous. We elucidate two definitions—termed “mean” and “galactic” α-enhancement—in more detail. While a star-forming galaxy has a high “mean” α-enhancement when its stars formed rapidly, a galaxy with a large “galactic” α-enhancement generally had a delayed SFH. We find that absorption-line strengths of Mg and Fe correlate with variations in α-enhancement. These correlations are strongest for the “galactic” α-enhancement. However, we show that these are mostly caused by other effects that are cross-correlated with α-enhancement, such as variations in the light-weighted age. This severely complicates the retrieval of α-enhancements in star-forming galaxies. The ambiguity is not severe for passive galaxies, and we confirm that spectral variations in these galaxies are caused by measurable variations in α-enhancements. We suggest that this more complex coupling between α-enhancement and SFHs can guide the interpretation of new observations of star-forming galaxies.

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10

Sharma, Sahil, Abhisek Sinha, Vandana Sharma, and Ram gopal Sharma. "Field Enhancement in Nanoparticles Due to IR Vortex Beams." ECS Transactions 107, no. 1 (April 24, 2022): 1255–69. http://dx.doi.org/10.1149/10701.1255ecst.

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In this report we present our study of interaction of light carrying OAM (Orbital Angular Momentum) with nanometric metallic discs. Plasmonic eﬀects are known to give rise to high local ﬁeld enhancement factors in gold nano-discs. These high intensities near ﬁelds have found use in a wide variety of imaging and detection applications. The local ﬁeld enhancement factor near the surface of the disc was calculated numerically using ﬁnite element method using the Comsol package. We report a signiﬁcant increase in the local ﬁeld enhancement factor for light beams carrying OAM compared to Gaussian beams which are attributed to Localised Surface Plasmon Resonances (LSPR). Such large enhancements in the ﬁeld can be immensely useful in the ﬁeld on near ﬁeld microscopy and electron generation.

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11

Bai, Minyu, Huan Liu, Fei Xie, Jijie Zhao, Weiguo Liu, and Huikai Xie. "Light trapping enhancement via structure design." International Journal of Modern Physics B 34, no. 06 (February 27, 2020): 2050040. http://dx.doi.org/10.1142/s021797922050040x.

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Light trapping is of great importance in many applications including photodetectors and solar cells. Silicon-based structures and hybrid devices were designed and studied to reduce reflection, thus enhance light trapping. The typical pillar array was analyzed concerning the pillar radius and distance between pillars first. The result showed that light reflection could be reduced from the range of 0.35–0.45 to the range of 0–0.3 with wavelength from 400 to 700 nm. What should be noted is that optimal size for light trapping changed when wavelength varied. Furthermore, hybrid structure was designed to increase light trapping. The results showed that the structure with random quantum dots (QDs) covering pillar array coated with two-dimensional (2D) material is an effective way to confine the light reflection under 0.1, thus promoting light trapping.

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12

Sodha, Mahendra Singh, Sweta Srivastava, and Rashmi Mishra. "Enhancement of thermionic emission by light." European Physical Journal Applied Physics 77, no. 2 (February 2017): 20101. http://dx.doi.org/10.1051/epjap/2017160206.

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13

Dianat, Pouya, Anna Persano, Fabio Quaranta, Adriano Cola, and Bahram Nabet. "Anomalous Capacitance Enhancement Triggered by Light." IEEE Journal of Selected Topics in Quantum Electronics 21, no. 4 (July 2015): 1–5. http://dx.doi.org/10.1109/jstqe.2014.2376701.

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14

Xie, Junyi, Hao Bian, Yuanhang Wu, Yu Zhao, Linmin Shan, and Shijie Hao. "Semantically-guided low-light image enhancement." Pattern Recognition Letters 138 (October 2020): 308–14. http://dx.doi.org/10.1016/j.patrec.2020.07.041.

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15

Dean, Jacob C., Tihana Mirkovic, Zi S. D. Toa, Daniel G. Oblinsky, and Gregory D. Scholes. "Vibronic Enhancement of Algae Light Harvesting." Chem 1, no. 6 (December 2016): 858–72. http://dx.doi.org/10.1016/j.chempr.2016.11.002.

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16

Gadomsky, O. N., I. V. Gadomskaya, and K. K. Altunin. "Giant light enhancement in atomic clusters." Journal of Experimental and Theoretical Physics 109, no. 1 (July 2009): 23–28. http://dx.doi.org/10.1134/s1063776109070036.

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17

Hosu, Vlad, Mai Lan Ha, and Terence Sim. "Light montage for perceptual image enhancement." Computer Graphics Forum 33, no. 2 (May 2014): 185–94. http://dx.doi.org/10.1111/cgf.12292.

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18

Zhou, Chu, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, and Boxin Shi. "Polarization-Aware Low-Light Image Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3742–50. http://dx.doi.org/10.1609/aaai.v37i3.25486.

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Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the angle of polarization (AoP). Existing low-light image enhancement methods cannot handle the polarized images well since they operate in the intensity domain, without effectively exploiting the information provided by polarization. In this paper, we propose a Stokes-domain enhancement pipeline along with a dual-branch neural network to handle the problem in a polarization-aware manner. Two application scenarios (reflection removal and shape from polarization) are presented to show how our enhancement can improve their results.

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19

Liang, Xiwen, and Xiaoyan Chen. "Enhancement methodology for low light image." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 12–19. http://dx.doi.org/10.5954/icarob.2023.ps3.

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20

Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3457905.

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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

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21

Huang, Huamao, Jinyong Hu, and Hong Wang. "Light-Output Enhancement of GaN-Based Light-Emitting Diodes with Three-Dimensional Backside Reflectors Patterned by Microscale Cone Array." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/837586.

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Three-dimensional (3D) backside reflector, compared with flat reflectors, can improve the probability of finding the escape cone for reflecting lights and thus enhance the light-extraction efficiency (LEE) for GaN-based light-emitting diode (LED) chips. A triangle-lattice of microscale SiO2cone array followed by a 16-pair Ti3O5/SiO2distributed Bragg reflector (16-DBR) was proposed to be attached on the backside of sapphire substrate, and the light-output enhancement was demonstrated by numerical simulation and experiments. The LED chips with flat reflectors or 3D reflectors were simulated using Monte Carlo ray tracing method. It is shown that the LEE increases as the reflectivity of backside reflector increases, and the light-output can be significantly improved by 3D reflectors compared to flat counterparts. It can also be observed that the LEE decreases as the refractive index of the cone material increases. The 3D 16-DBR patterned by microscale SiO2cone array benefits large enhancement of LEE. This microscale pattern was prepared by standard photolithography and wet-etching technique. Measurement results show that the 3D 16-DBR can provide 12.1% enhancement of wall-plug efficiency, which is consistent with the simulated value of 11.73% for the enhancement of LEE.

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22

Wang, Yun-Fei, He-Ming Liu, and Zhao-Wang Fu. "Low-Light Image Enhancement via the Absorption Light Scattering Model." IEEE Transactions on Image Processing 28, no. 11 (November 2019): 5679–90. http://dx.doi.org/10.1109/tip.2019.2922106.

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23

Zhang, Xiaojin, Zhiqun He, Chunjun Liang, Yongsheng Wang, Qixin Zhuang, and Zhewen Han. "Trap-induced light enhancement from a polymer light emitting device." Applied Physics Letters 103, no. 4 (July 22, 2013): 043306. http://dx.doi.org/10.1063/1.4816505.

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24

Jiang, Yonglong, Liangliang Li, Jiahe Zhu, Yuan Xue, and Hongbing Ma. "DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement." Tsinghua Science and Technology 28, no. 4 (August 2023): 743–53. http://dx.doi.org/10.26599/tst.2022.9010047.

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25

Yan, Jiaquan, Yijian Wang, Haoyi Fan, Jiayan Huang, Antoni Grau, and Chuansheng Wang. "LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement." Journal of Marine Science and Engineering 11, no. 6 (June 8, 2023): 1195. http://dx.doi.org/10.3390/jmse11061195.

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Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.

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26

Shi, Yangming, Xiaopo Wu, and Ming Zhu. "Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2258/1/012003.

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Abstract To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.

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27

Yu, Renwen, and Shanhui Fan. "Flashing light with nanophotonics." Science 375, no. 6583 (February 25, 2022): 822–23. http://dx.doi.org/10.1126/science.abn8478.

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28

Liu, Weiqiang, Peng Zhao, Xiangying Song, and Bo Zhang. "A Survey of Low-light Image Enhancement." Frontiers in Computing and Intelligent Systems 1, no. 3 (October 30, 2022): 88–92. http://dx.doi.org/10.54097/fcis.v1i3.2242.

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With the higher requirements of computer vision image enhancement of low-light image has become an important research content of computer vision. Traditional low-light image enhancement algorithms can improve image brightness and detailed visibility to varying degrees, but due to their strict mathematical derivation, such methods have bottlenecks and are difficult to break through their limits. With the development of deep learning and the birth of large-scale data sets, low-light image enhancement based on deep learning has become the mainstream trend. In this paper, first of all, the traditional low-light image enhancement algorithms are classified, summarized the improvement process of the traditional method, then the image enhancement method based on the deep learning are introduced, at the same time on the network structure and is suitable for the method of combing the network part, after the introduction to the experiment database and enhance image evaluation criteria. Based on the discussion of the above situation, combined with the actual situation, this paper points out the limitations of the current technology, and predicts its development trend.

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29

M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (July 30, 2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.

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30

Liu, Lin, Junfeng An, Jianzhuang Liu, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, Yan Feng Wang, and Qi Tian. "Low-Light Video Enhancement with Synthetic Event Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1692–700. http://dx.doi.org/10.1609/aaai.v37i2.25257.

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Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. However, these algorithms, based on the framework of multi-frame alignment and enhancement, may produce multi-frame fusion artifacts when encountering extreme low light or fast motion. In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events from multiple frames to guide the enhancement and restoration of low-light videos. Our method contains three stages: 1) event synthesis and enhancement, 2) event and image fusion, and 3) low-light enhancement. In this framework, we design two novel modules (event-image fusion transform and event-guided dual branch) for the second and third stages, respectively. Extensive experiments show that our method outperforms existing low-light video or single image enhancement approaches on both synthetic and real LLVE datasets. Our code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/LLVE-SEG.

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31

Zhao, Gang, Tianyi Tan, Yishu Zhu, Min Hu, and Chunsheng Zhao. "Method to quantify black carbon aerosol light absorption enhancement with a mixing state index." Atmospheric Chemistry and Physics 21, no. 23 (December 9, 2021): 18055–63. http://dx.doi.org/10.5194/acp-21-18055-2021.

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Abstract. Large uncertainties remain when estimating the warming effects of ambient black carbon (BC) aerosols on climate. One of the key challenges in modeling the radiative effects is predicting the BC light absorption enhancement, which is mainly determined by the mass ratio (MR) of non-BC coating material to BC in the population of BC-containing aerosols. For the same MR, recent research has found that the radiative absorption enhancements by BC are also controlled by its particle-to-particle heterogeneity. In this study, the BC mixing state index (χ) is developed to quantify the dispersion of ambient black carbon aerosol mixing states based on binary systems of BC and other non-black carbon components. We demonstrate that the BC light absorption enhancement increases with χ for the same MR, which indicates that χ can be employed as a factor to constrain the light absorption enhancement of ambient BC. Our framework can be further used in the model to study the radiative effects of black carbon on climate change.

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32

Xi, Yang, Zihao Zhang, and Wenjing Wang. "Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression." Applied Sciences 13, no. 17 (August 25, 2023): 9645. http://dx.doi.org/10.3390/app13179645.

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Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.

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33

Xia, Zihuan, Yonggang Wu, Renchen Liu, Pinglin Tang, and Zhaoming Liang. "Light trapping enhancement with combined front metal nanoparticles and back dif fraction gratings." Chinese Optics Letters 11, S1 (2013): S10503. http://dx.doi.org/10.3788/col201311.s10503.

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34

Kirschbaum, Miko U. F., and Suzanne M. Lambie. "Re-analysis of plant CO2 responses during the exponential growth phase: interactions with light, temperature, nutrients and water availability." Functional Plant Biology 42, no. 10 (2015): 989. http://dx.doi.org/10.1071/fp15103.

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Many short-term experiments have been conducted under increasing CO2 but results have been varied and have not yet led to a conclusive quantitative understanding of the CO2 response of plant growth. This may have been partly due to a lack of explicit consideration of the positive feedback inherent in plant growth during periods of exponential growth. This feedback can increase an initial physiological enhancement of relative growth rate (RGR) into a much larger biomass enhancement. To overcome this problem, we re-analysed existing experimental data from 78 publications. We calculated the RGRs of C3 plants and their relative enhancement under elevated CO2 and derived response indices that were independent of the duration of experiments and the RGR at normal atmospheric CO2. The RGR of unstressed plants increased by 14 ± 2% under doubled CO2, with observed RGR enhancement linearly correlated with calculated photosynthetic enhancements (based on the Farquhar-von Caemmerer-Berry photosynthesis model), but at only half their numeric values. Calculated RGR enhancements did not change significantly for temperatures from 12 to 40°C, but were reduced under nutrient limitation, and were increased under water stress or low irradiance. We concluded that short-term experiments can offer simple and cost-effective insights into plant CO2 responses, provided they are analysed by calculating relative changes in RGR during the strictly exponential initial growth phase.

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35

Jiang, Yifan, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. "EnlightenGAN: Deep Light Enhancement Without Paired Supervision." IEEE Transactions on Image Processing 30 (2021): 2340–49. http://dx.doi.org/10.1109/tip.2021.3051462.

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36

Liu, Jiaying, Dejia Xu, Wenhan Yang, Minhao Fan, and Haofeng Huang. "Benchmarking Low-Light Image Enhancement and Beyond." International Journal of Computer Vision 129, no. 4 (January 11, 2021): 1153–84. http://dx.doi.org/10.1007/s11263-020-01418-8.

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37

Khan, Rizwan, You Yang, Qiong Liu, Jialie Shen, and Bing Li. "Deep image enhancement for ill light imaging." Journal of the Optical Society of America A 38, no. 6 (May 17, 2021): 827. http://dx.doi.org/10.1364/josaa.410316.

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38

Gramuglia, Francesco, Simone Frasca, Emanuele Ripiccini, Esteban Venialgo, Valentin Gâté, Hind Kadiri, Nicolas Descharmes, Daniel Turover, Edoardo Charbon, and Claudio Bruschini. "Light Extraction Enhancement Techniques for Inorganic Scintillators." Crystals 11, no. 4 (March 30, 2021): 362. http://dx.doi.org/10.3390/cryst11040362.

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Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical Positron Emission Tomography (PET) rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing Distributed Bragg Reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

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Salahieh, Basel, Yi Wu, and Oscar Nestares. "Light Field Perception Enhancement for Integral Displays." Electronic Imaging 2018, no. 5 (January 28, 2018): 269–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.05.pmii-269.

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Wang, Yufei, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot. "Low-Light Image Enhancement with Normalizing Flow." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2604–12. http://dx.doi.org/10.1609/aaai.v36i3.20162.

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To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. In this way, the conditional distribution of the normally exposed images can be well modeled, and the enhancement process, i.e., the other inference direction of the invertible network, is equivalent to being constrained by a loss function that better describes the manifold structure of natural images during the training. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.

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Liang, Hong, Ankang Yu, Mingwen Shao, and Yuru Tian. "Multi-Feature Guided Low-Light Image Enhancement." Applied Sciences 11, no. 11 (May 29, 2021): 5055. http://dx.doi.org/10.3390/app11115055.

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Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

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Huang, Haofeng, Wenhan Yang, Yueyu Hu, Jiaying Liu, and Ling-Yu Duan. "Towards Low Light Enhancement With RAW Images." IEEE Transactions on Image Processing 31 (2022): 1391–405. http://dx.doi.org/10.1109/tip.2022.3140610.

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Schneider, Thomas, Markus Junker, and Kai-Uwe Lauterbach. "Potential ultra wide slow-light bandwidth enhancement." Optics Express 14, no. 23 (2006): 11082. http://dx.doi.org/10.1364/oe.14.011082.

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Wang, Li-Wen, Zhi-Song Liu, Wan-Chi Siu, and Daniel P. K. Lun. "Lightening Network for Low-Light Image Enhancement." IEEE Transactions on Image Processing 29 (2020): 7984–96. http://dx.doi.org/10.1109/tip.2020.3008396.

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Brenier, R. "Enhancement of Light Transmission through Silver Nanoparticles." Journal of Physical Chemistry C 116, no. 9 (February 27, 2012): 5358–66. http://dx.doi.org/10.1021/jp210374j.

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Gadomsky, O. N., I. V. Gadomskaya, and K. K. Altunin. "Giant enhancement of light in atomic clusters." JETP Letters 90, no. 4 (October 2009): 244–50. http://dx.doi.org/10.1134/s002136400916005x.

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Ramanna, Luveshan, Ismail Rawat, and Faizal Bux. "Light enhancement strategies improve microalgal biomass productivity." Renewable and Sustainable Energy Reviews 80 (December 2017): 765–73. http://dx.doi.org/10.1016/j.rser.2017.05.202.

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Toyosawa, Naoko, and Keiji Tanaka. "Photocurrent enhancement in light-soaked chalcogenide glasses." Physical Review B 56, no. 12 (September 15, 1997): 7416–21. http://dx.doi.org/10.1103/physrevb.56.7416.

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Hokr, Brett H., and Vladislav V. Yakovlev. "Raman signal enhancement via elastic light scattering." Optics Express 21, no. 10 (May 7, 2013): 11757. http://dx.doi.org/10.1364/oe.21.011757.

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Wu, Zhaoxin, Liduo Wang, and Yong Qiu. "Contrast-enhancement in organic light-emitting diodes." Optics Express 13, no. 5 (2005): 1406. http://dx.doi.org/10.1364/opex.13.001406.

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