To accurately assess the performance of cooperative multiple packet reception （MPR） based on network-assisted diversity multiple access （NDMA）, non-ideal collision detection is introduced in ALLIANCES （ALLow impr...To accurately assess the performance of cooperative multiple packet reception （MPR） based on network-assisted diversity multiple access （NDMA）, non-ideal collision detection is introduced in ALLIANCES （ALLow improved access in the network via cooperation and energy savings）. To provide a unified anatysis frame- work, the length of cooperative transmission epoch is fixed to the detected collision order. The mathematical analysis of potential throughput （PTP） and potential packet loss rate （PPLR） are given under a pessimistic assumption and an optimistic assumption. According to the analysis of PTP and PPLR, threshold selection is done to optimize system performances, e.g. the optimal threshold should guarantee PTP to be maximum or guarantee PPLR to be minimum. In simulations, the thresholds are selected according to PTP under the pessimistic assumption. Simulation results show that the proposed cooperative MPR scheme can achieve higher throughput than NDMA and slotted ALOHA schemes.展开更多
Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the...Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.展开更多
With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery dete...With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.展开更多
基金supported by the National Natural Science Foundation of China(60972039)the National High-Tech Research and Development Program of China(2009AA01Z241)+2 种基金the Key Grant and Basic Research Programs of Natural Science Fund for Higher Education of Jiangsu Province(06KJA51001)the Project Key Grant Research Programs of Natural Science Fund of Science and Technology Department of Jiangsu Province(BK2007729)the Natural Science Fund for Higher Education of Jiangsu Province(09KJB510012).
文摘To accurately assess the performance of cooperative multiple packet reception （MPR） based on network-assisted diversity multiple access （NDMA）, non-ideal collision detection is introduced in ALLIANCES （ALLow improved access in the network via cooperation and energy savings）. To provide a unified anatysis frame- work, the length of cooperative transmission epoch is fixed to the detected collision order. The mathematical analysis of potential throughput （PTP） and potential packet loss rate （PPLR） are given under a pessimistic assumption and an optimistic assumption. According to the analysis of PTP and PPLR, threshold selection is done to optimize system performances, e.g. the optimal threshold should guarantee PTP to be maximum or guarantee PPLR to be minimum. In simulations, the thresholds are selected according to PTP under the pessimistic assumption. Simulation results show that the proposed cooperative MPR scheme can achieve higher throughput than NDMA and slotted ALOHA schemes.
基金The National Natural Science Foundation of China(No.62261032)the Central Government Guiding Funds for Local Scienceand Technology Development Program(No.25ZYJA026).
文摘Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.
基金supported in part by the National Natural Science Foundation of China(Grant Number 61971078)Chongqing University of Technology Graduate Innovation Foundation(Grant Number gzlcx20222064).
文摘With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.
