This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to imp...This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.展开更多
Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances ...Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances of moving objects vary dramatically,the conventional MDCNN model will suffer from the model drift problem.To solve such problem in tracking rapid objects under limiting environment for MDCNN model,this paper proposed an auto-attentional mechanism-based MDCNN(AA-MDCNN)model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approach-First,to distinguish the foreground object between background and other similar objects,the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other.Then,the bidirectional gated recurrent unit(Bi-GRU)architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps.Finally,the final feature map is obtained by fusion the above two feature maps for object tracking.In addition,a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.Findings-In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model,this paper used ImageNet-Vid dataset to train the object tracking model,and the OTB-50 dataset is used to validate the AA-MDCNN tracking model.Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75%and success rate 2.41%,respectively.In addition,the authors also selected six complex tracking scenarios in OTB-50 dataset;over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes.In addition,except for the scenario of multi-objects moving with each other,the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/value-This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features.By using the proposed AA-MDCNN model,rapid object tracking under complex background,motion blur and occlusion objects has better effect,and such model is expected to be further applied to the rapid object tracking in the real world.展开更多
基金support from the following institutional grant.Internal Grant Agency of the Faculty of Economics and Management,Czech University of Life Sciences Prague,grant no.2023A0004(https://iga.pef.czu.cz/,accessed on 6 June 2025).
文摘This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.
基金supported by the Education and Scientific Research Project for Young and Middle-aged Teachers in Fujian Province(No.JAT200581).
文摘Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances of moving objects vary dramatically,the conventional MDCNN model will suffer from the model drift problem.To solve such problem in tracking rapid objects under limiting environment for MDCNN model,this paper proposed an auto-attentional mechanism-based MDCNN(AA-MDCNN)model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approach-First,to distinguish the foreground object between background and other similar objects,the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other.Then,the bidirectional gated recurrent unit(Bi-GRU)architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps.Finally,the final feature map is obtained by fusion the above two feature maps for object tracking.In addition,a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.Findings-In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model,this paper used ImageNet-Vid dataset to train the object tracking model,and the OTB-50 dataset is used to validate the AA-MDCNN tracking model.Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75%and success rate 2.41%,respectively.In addition,the authors also selected six complex tracking scenarios in OTB-50 dataset;over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes.In addition,except for the scenario of multi-objects moving with each other,the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/value-This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features.By using the proposed AA-MDCNN model,rapid object tracking under complex background,motion blur and occlusion objects has better effect,and such model is expected to be further applied to the rapid object tracking in the real world.
