We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clusterin...
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The paper introduces a Kernel Weibull M-Transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-Transformation into the cost f...
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The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on met...
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The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.
Rapid large-area deep tissue imaging at long working distances is important for clinical diagnosis. A near-infrared metalens-based hybrid probe con-focal micro-endoscope (pCM) objective with a large field of view of 5...
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Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics researc...
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Deep learning techniques have significantly improved image restoration tasks in recent *** a crucial compo-nent of deep learning,the loss function plays a key role in network optimization and performance ***,the curre...
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Deep learning techniques have significantly improved image restoration tasks in recent *** a crucial compo-nent of deep learning,the loss function plays a key role in network optimization and performance ***,the currently prevalent loss functions assign equal weight to each pixel point during loss calculation,which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics *** address this issue,this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery *** novel loss function can adjust the weight of the reconstruction loss based on the grey value of different pixel points,thereby effectively optimizing the network training by differentially utilizing the grey information from the original ***,we calculate a weight factor for each pixel point based on its grey value and combine it with the reconstruction loss to create a new loss *** ensures that pixel points with smaller grey values receive greater attention,improving network *** order to verify the effectiveness of the proposed asymmetric loss function,we conducted experimental tests in the image super-resolution *** experimental results show that the model with the introduction of asymmetric loss weights improves all the indexes of the processing results without increasing the training *** the typical super-resolution network SRCNN,by introducing asymmetric weights,it is possible to improve the peak signal-to-noise ratio(PSNR)by up to about 0.5%,the structural similarity index(SSIM)by up to about 0.3%,and reduce the root-mean-square error(RMSE)by up to about 1.7%with essentially no increase in training *** addition,we also further tested the performance of the proposed method in the denoising task to verify the potential applicability of the method in the image restoration task.
Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in...
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Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification due to their strong feature extraction capabilities. Nevertheless, CNN-based classification methods face challenges in c...
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The phase recovery method based on the Transport of Intensity Equation is widely used in the field of microscopic imaging of biological cells. When using this method for phase recovery, the CCD is always moved to acqu...
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Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *...
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Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *** the existing influence maximization methods,the community‐based ones can achieve a good balance between effectiveness and ***,this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping *** fact,many nodes in social networks are overlapping ones,which play more important role in influence *** this end,an overlapping community‐based particle swarm opti-mization algorithm named OCPSO for influence maximization in social networks,which can make full use of overlapping nodes,non‐overlapping nodes,and their interactive information is ***,an overlapping community detection algorithm is used to obtain the information of overlapping community structures,based on which three novel evolutionary strategies,such as initialisation,mutation,and local search are designed in OCPSO for better finding influential *** results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts(***,CMA‐IM,CIM,CDH‐SHRINK,CNCG,and CFIN).
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