Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images...
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Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.
High-entropy effect is a novel design strategy to optimize properties and explore novel *** this work,(La_(1/5)Nd_(1/5)Sm_(1/5)Ho_(1/5)Y_(1/5))NbO_(4)(5RNO)high-entropy microwave dielectric ceramics were successfully ...
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High-entropy effect is a novel design strategy to optimize properties and explore novel *** this work,(La_(1/5)Nd_(1/5)Sm_(1/5)Ho_(1/5)Y_(1/5))NbO_(4)(5RNO)high-entropy microwave dielectric ceramics were successfully prepared in the sintering temperature(S.T.)range of 1210–1290℃via a solid-phase reaction route,and medium-entropy(La_(1/3)Nd_(1/3)Sm_(1/3))NbO_(4) and(La_(1/4)Nd_(1/4)Sm_(1/4)Ho_(1/4))NbO_(4)(3RNO and 4RNO)ceramics were *** effects of the entropy(S)on crystal structure,phase transition,and dielectric performance were *** entropy increase yields a significant increase in a phase transition temperature(from monoclinic fergusonite to tetragonal scheelite structure).Optimal microwave dielectric properties were achieved in the high-entropy ceramics(5RNO)at the sintering temperature of 1270℃for 4 h with a relative density of 98.2%and microwave dielectric properties of dielectric permittirity(ε_(r))=19.48,quality factor(Q×f)=47,770 GHz,and resonant frequency temperature coefficient(τ_(f))=–13.50 ppm/℃.This work opens an avenue for the exploration of novel microwave dielectric material and property optimization via entropy engineering.
Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiov...
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Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we des...
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Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we design a privacy-preservation-aware delay optimization task-offloading algorithm(PPDO)in a CEC *** considering location and usage pattern privacy protection,we establish a privacy task model to interfere with the edge server and ensure user *** address the extra delay arising from privacy protection,we subsequently leverage a Markov decision processing(MDP)policy-iteration-based algorithm to minimize delays without compromising *** simultaneously accelerate the MDP operation,we develop an extension that improves the PPDO by optimizing the action ***,a comprehensive simulation was conducted using the edge user allocation(EUA)*** results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing ***,we examined the advantages and disadvantages of improving PPDO.
Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline c...
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Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource *** proposed an integrated prediction method of stacking container ...
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Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource *** proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource *** variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion *** permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of ***,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization *** Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.
There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of netw...
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There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.
Video surveillance plays a crucial role in improving people's well-being and reducing social crime rates. In order to assist observers to improve the efficiency of surveillance, a large number of scholars have use...
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With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online *** of the current research uses the commen...
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With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online *** of the current research uses the comments of the learning platform or the learner’s expression for emotion *** research data on other modalities are *** of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on *** of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning *** data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they *** the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is *** method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological *** method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow *** model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep ***,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based ***-based features are used to supplement and enhance the deep features of physiological ***,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,*** the four public data sets,our proposed method also achieves bett
In the big data environment, customer information is lengthy and complex, and lenders need to connect with users on the Internet. Therefore, higher requirements are required for the identification of customers. In vie...
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