This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualiz...This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.展开更多
Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and har...Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.展开更多
Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremend...Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.展开更多
In this paper, boundedness and compactness of the composition operator on the generalized Lipschitz spaces Λα （α 〉 1） of holomorphic functions in the unit disk are characterized.
Dear Editor.This letter presents a normalization mechanism to effectively fuse infrared and visible images in an encoder-decoder network.Source images are decomposed into source-invariant structure and sourcespecific ...Dear Editor.This letter presents a normalization mechanism to effectively fuse infrared and visible images in an encoder-decoder network.Source images are decomposed into source-invariant structure and sourcespecific detail features.Then,the information of detail features is sufficiently incorporated into the structure features using this normalization mechanism in the decoder,which generates high-contrast fused images with highlighted targets and abundant texture information.Qualitative and quantitative experiments on two challenging datasets demonstrate the superiority of our method over current stateof-the-art methods.展开更多
With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to ed...With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.展开更多
Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to countin...Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning.The collection of data required for model training is also a tedious and expensive work.To address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is proposed.The algorithm uses VGG16 as the backbone to extract image features.Auxiliary tasks based on density mutual exclusion are introduced.The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data.In addition,a density difference loss is designed.The feature representation is enhanced by amplifying the difference of features between different density levels.The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors.Compared with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,respectively.The code is available at.展开更多
Manipulation of spontaneous emission from an atom confined in three kinds of modified reservoirs has been investigated by means of an elliptically polarized laser field. Some interesting phenomena such as the multi-pe...Manipulation of spontaneous emission from an atom confined in three kinds of modified reservoirs has been investigated by means of an elliptically polarized laser field. Some interesting phenomena such as the multi-peak structure, extreme spectral narrowing, and cancellation of spontaneous emission can be observed by adjusting controllable system parameters. Moreover, these phenomena depend on the constructive or destructive quantum interference between multiple decay channels and which can be changed appreciably by varying the phase difference between the two circularly polarized components of the probe field. These results demonstrate the importance of an elliptially polarized laser field in controlling the spontaneous emission and its potential applications in high-precision spectroscopy.展开更多
Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high pe...Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique's feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.展开更多
Automatic protocol mining is a promising approach for inferring accurate and complete API protocols. However, just as with any data-mining technique, this approach requires sufficient training data（object usage scena...Automatic protocol mining is a promising approach for inferring accurate and complete API protocols. However, just as with any data-mining technique, this approach requires sufficient training data（object usage scenarios）. Existing approaches resolve the problem by analyzing more programs, which may cause significant runtime overhead. In this paper, we propose an inheritance-based oversampling approach for object usage scenarios（OUSs）. Our technique is based on the inheritance relationship in object-oriented programs. Given an object-oriented program p, generally, the OUSs that can be collected from a run of p are not more than the objects used during the run. With our technique, a maximum of n times more OUSs can be achieved, where n is the average number of super-classes of all general OUSs. To investigate the effect of our technique, we implement it in our previous prototype tool, ISpec Miner, and use the tool to mine protocols from several real-world programs. Experimental results show that our technique can collect 1.95 times more OUSs than general approaches. Additionally, accurate and complete API protocols are more likely to be achieved. Furthermore, our technique can mine API protocols for classes never even used in programs, which are valuable for validating software architectures, program documentation, and understanding. Although our technique will introduce some runtime overhead, it is trivial and acceptable.展开更多
Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local informa...Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.展开更多
基金supported in part by the National Natural Science Foundation of China[62301374]Hubei Provincial Natural Science Foundation of China[2022CFB804]+2 种基金Hubei Provincial Education Research Project[B2022057]the Youths Science Foundation of Wuhan Institute of Technology[K202240]the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology[CX2023295].
文摘This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.
基金supported by the National Natural Science Foundation of China(No.61772386)National Key Research and Development Project(No.2018YFB1305001)Fundamental Research Funds for the Central Universities(No.KJ02072021-0119).
文摘Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.
基金supported by the Open-Fund of WNLO (Grant No.2018WNLOKF027)the Hubei Key Laboratory of Intelligent Robot in Wuhan Institute of Technology (Grant No.HBIRL 202003).
文摘Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.
基金Supported in part by the National Natural Science Foundation of China (10971219)
文摘In this paper, boundedness and compactness of the composition operator on the generalized Lipschitz spaces Λα （α 〉 1） of holomorphic functions in the unit disk are characterized.
基金supported in part by the National Natural Science Foundation of China(62171327,61771353)the first batch of application basic technology and science research foundation in Hubei Nuclear Power Operation Engineering Technology Research Center(B210610)the Hubei Three Gorges Laboratory Open Fund(SC215001)。
文摘Dear Editor.This letter presents a normalization mechanism to effectively fuse infrared and visible images in an encoder-decoder network.Source images are decomposed into source-invariant structure and sourcespecific detail features.Then,the information of detail features is sufficiently incorporated into the structure features using this normalization mechanism in the decoder,which generates high-contrast fused images with highlighted targets and abundant texture information.Qualitative and quantitative experiments on two challenging datasets demonstrate the superiority of our method over current stateof-the-art methods.
基金Supported by the National Natural Science Foundation of China(62102292)the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology) of China(HBIRL202103,HBIRL202204)+1 种基金Science Foundation Research Project of Wuhan Institute of Technology of China(K202035)Graduate Innovative Fund of Wuhan Institute of Technology of China(CX2021265)。
文摘With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.
基金supported in part by National Natural Science Foundation of China under Grant 61906139in part by Knowledge Innovation Program of Wuhan-Shuguang Project under Grant 2022010801020359+1 种基金in part by the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology)of China under Grant HBIRL 202108in part by Graduate Innovative Fund of Wuhan Institute of Technology under Grant CX2022336.
文摘Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning.The collection of data required for model training is also a tedious and expensive work.To address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is proposed.The algorithm uses VGG16 as the backbone to extract image features.Auxiliary tasks based on density mutual exclusion are introduced.The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data.In addition,a density difference loss is designed.The feature representation is enhanced by amplifying the difference of features between different density levels.The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors.Compared with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,respectively.The code is available at.
基金Supported by the National Natural Science Foundation of China under Grant Nos.11004069 and 91021011the Doctoral Foundation of the Ministry of Education of China under Grant No.20100142120081the National Basic Research Program of China under Grant No.2012CB922103
文摘Manipulation of spontaneous emission from an atom confined in three kinds of modified reservoirs has been investigated by means of an elliptically polarized laser field. Some interesting phenomena such as the multi-peak structure, extreme spectral narrowing, and cancellation of spontaneous emission can be observed by adjusting controllable system parameters. Moreover, these phenomena depend on the constructive or destructive quantum interference between multiple decay channels and which can be changed appreciably by varying the phase difference between the two circularly polarized components of the probe field. These results demonstrate the importance of an elliptially polarized laser field in controlling the spontaneous emission and its potential applications in high-precision spectroscopy.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2013AA12A202)the National Natural Science Foundation of China(Nos.61172173,41501505,and 61502205)+1 种基金the Natural Science Foundation of Hubei Province,China(No.2014CFB779)the Youths Science Foundation of Wuhan Institute of Technology(No.K201546)
文摘Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique's feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.
基金supported by the Scientific Research Project of the Education Department of Hubei Province,China(No.Q20181508)the Youths Science Foundation of Wuhan Institute of Technology(No.k201622)+5 种基金the Surveying and Mapping Geographic Information Public Welfare Scientific Research Special Industry(No.201412014)the Educational Commission of Hubei Province,China(No.Q20151504)the National Natural Science Foundation of China(Nos.41501505,61502355,61502355,and 61502354)the China Postdoctoral Science Foundation(No.2015M581887)the Key Program of Higher Education Institutions of Henan Province,China(No.17A520040)and the Natural Science Foundation of Henan Province,China(No.162300410177)
文摘Automatic protocol mining is a promising approach for inferring accurate and complete API protocols. However, just as with any data-mining technique, this approach requires sufficient training data（object usage scenarios）. Existing approaches resolve the problem by analyzing more programs, which may cause significant runtime overhead. In this paper, we propose an inheritance-based oversampling approach for object usage scenarios（OUSs）. Our technique is based on the inheritance relationship in object-oriented programs. Given an object-oriented program p, generally, the OUSs that can be collected from a run of p are not more than the objects used during the run. With our technique, a maximum of n times more OUSs can be achieved, where n is the average number of super-classes of all general OUSs. To investigate the effect of our technique, we implement it in our previous prototype tool, ISpec Miner, and use the tool to mine protocols from several real-world programs. Experimental results show that our technique can collect 1.95 times more OUSs than general approaches. Additionally, accurate and complete API protocols are more likely to be achieved. Furthermore, our technique can mine API protocols for classes never even used in programs, which are valuable for validating software architectures, program documentation, and understanding. Although our technique will introduce some runtime overhead, it is trivial and acceptable.
基金supported by National Natural Science Foundation of China[42090012]Guangxi Science and Technology Plan Project(Guike 2021AB30019)+4 种基金Hubei Province Key R\&D Project(2022BAA048)Sichuan Province Key R\&D Project(2022YFN0031,2023YFN0022,2023YFS0381)Zhuhai Industry-University-Research Cooperation Project(ZH22017001210098PWC)Shanxi Provincial Science and Technology Major Special Project(202201150401020)Guangxi Key Laboratory of Spatial Information and Surveying and Mapping Fund Project(21-238-21-01).
文摘Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.
