Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by addit...Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.展开更多
Hyperentanglement is a promising resource in quantum information processing with its high capacity character, defined as the entanglement in multiple degrees of freedom(DOFs) of a quantum system, such as polarization,...Hyperentanglement is a promising resource in quantum information processing with its high capacity character, defined as the entanglement in multiple degrees of freedom(DOFs) of a quantum system, such as polarization, spatial-mode, orbit-angular-momentum, time-bin and frequency DOFs of photons.Recently, hyperentanglement attracts much attention as all the multiple DOFs can be used to carry information in quantum information processing fully. In this review, we present an overview of the progress achieved so far in the field of hyperentanglement in photon systems and some of its important applications in quantum information processing, including hyperentanglement generation, complete hyperentangled-Bell-state analysis, hyperentanglement concentration, and hyperentanglement purification for high-capacity long-distance quantum communication. Also, a scheme for hyper-controlled-not gate is introduced for hyperparallel photonic quantum computation, which can perform two controlled-not gate operations on both the polarization and spatial-mode DOFs and depress the resources consumed and the photonic dissipation.展开更多
基金This work was supported by the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2019-048)the Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology(BNRist)(No.BNR2019TD01022).
文摘Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.
基金supported by the National Natural Science Foundation of China (11474026, 11574038, 11547106, 11604226, and 11674033)
文摘Hyperentanglement is a promising resource in quantum information processing with its high capacity character, defined as the entanglement in multiple degrees of freedom(DOFs) of a quantum system, such as polarization, spatial-mode, orbit-angular-momentum, time-bin and frequency DOFs of photons.Recently, hyperentanglement attracts much attention as all the multiple DOFs can be used to carry information in quantum information processing fully. In this review, we present an overview of the progress achieved so far in the field of hyperentanglement in photon systems and some of its important applications in quantum information processing, including hyperentanglement generation, complete hyperentangled-Bell-state analysis, hyperentanglement concentration, and hyperentanglement purification for high-capacity long-distance quantum communication. Also, a scheme for hyper-controlled-not gate is introduced for hyperparallel photonic quantum computation, which can perform two controlled-not gate operations on both the polarization and spatial-mode DOFs and depress the resources consumed and the photonic dissipation.
