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.
Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and ***,it raises several questions about privacy,esp...
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Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and ***,it raises several questions about privacy,especially for users who prefer to remain anonymous,especially on online social networks(OSNs).Moreover,due to the commercialization of online users'data,some service providers sell users'data to third parties at the blind side of the users,which leads to trust issues between users and service *** matters call for a system that gives online users much-needed control and autonomy of their *** the advancement of blockchain technology,many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and *** resolve these limitations,we propose RecGuard,a privacy preservation blockchain-based network *** developed two smart contracts,RG-SH and RG-ST,to ensure the security and privacy of user *** RG-SH manages user data,whereas the RGST stores data.A graph convolutional network(GCN)was integrated with the blockchain-based system to detect malicious ***,we implemented our framework prototype on a locally simulated *** analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.
The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it...
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The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world ***,it shows deficiencies in terms of limited local expl...
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The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world ***,it shows deficiencies in terms of limited local exploration capabilities and less precise ***,this research aims to integrate the topological search(TS)mechanism with the gradient search rule(GSR)into the framework of RUN,introducing an enhanced algorithm called TGRUN to improve the performance of the original *** TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution,enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local *** prevent the algorithm from becoming trapped in local optima,the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution *** study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of ***,the evaluation includes real-world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the *** validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.
One of the main factor contributing to mental illness, which has been linked to an increased risk of dying young is depression. Additionally, it significantly contributes to suicide ideation. Although there are many u...
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In agriculture, detecting plant diseases is crucial for optimal plant growth. Initially, input images are collected from three datasets: banana leaf spot diseases (BananaLSD) dataset, banana leaf dataset, and PSFD-Mus...
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The goal of neural architecture search (NAS) is to either downsize the neural architecture and model of a deep neural network (DNN), adjust a neural architecture to improve its end result, or even speed up the whole t...
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The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal *** deep learning techniques such as LSTM,GRU and ESN...
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Organizations achieve successful investment risk management by achieving optimal risk exposures and return objectives at the same time. The research examines optimization methodologies through its comparison of SciPy&...
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Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. ...
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