feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input a...feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.展开更多
With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on context.Nowadays,image semantic inpainting techniques...With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on context.Nowadays,image semantic inpainting techniques are becoming more mature.However,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original image.Therefore,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)distance.With the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent years.We evaluated our algorithm on the ImageNet dataset.We obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image.展开更多
基金This work was supported in part by the Open Research Project of State Key Laboratory of Novel Software Technology under Grant KFKT2018B23the Priority Academic Program Development of Jiangsu Higher Education Institutions,the 2018 Tiancheng Huizhi Innovation Promotion Education and Scientific Research Innovation Fund of the Ministry of Education under Grant 2018A03038 and the Open Project Program of the State Key Lab of CAD&CG(Grant No.A1916),Zhejiang University.
文摘feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.
基金supported by the National Natural Science Foundation of China(Grant No.42075007)the Open Project of Provincial Key Laboratory for Computer Information Processing Technology under Grant KJS1935,Soochow University+1 种基金the Priority Academic Program Development of Jiangsu Higher Education InstitutionsGraduate Scientific Research Innovation Program of Jiangsu Province under Grant no.KYCX21_1015.
文摘With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on context.Nowadays,image semantic inpainting techniques are becoming more mature.However,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original image.Therefore,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)distance.With the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent years.We evaluated our algorithm on the ImageNet dataset.We obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image.
