Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.Howev...Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.展开更多
Intercropping of mulberry(Morus alba L.)and alfalfa(Medicago sativa L.)is a new forestry-grass compound model in China,which can provide high forage yields with high protein.Nitrogen application is one of the importan...Intercropping of mulberry(Morus alba L.)and alfalfa(Medicago sativa L.)is a new forestry-grass compound model in China,which can provide high forage yields with high protein.Nitrogen application is one of the important factors determining the production and quality of this system.To elucidate the advantages of intercropping and nitrogen application,we analyzed the changes of physicochemical properties,enzyme activities,and microbial communities in the rhizosphere soil.We used principal components analysis(PCA)and redundancy discriminators analysis to clarify the relationships among treatments and between treatments and environmental factors,respectively.The results showed that nitrogen application significantly increased pH value,available nitrogen content,soil water content(SWC),and urea(URE)activity in rhizosphere soil of monoculture mulberry.In contrast,intercropping and intercropping+N significantly decreased pH and SWC in mulberry treatments.Nitrogen,intercropping and intercropping+N sharply reduced soil organic matter content and SWC in alfalfa treatments.Nitrogen,intercropping,and intercropping+N increased the values of McIntosh diversity(U),Simpson diversity(D),and Shannon-Weaver diversity(H’)in mulberry treatments.However,PC A scatter plots showed clustering of monoculture mulberry with nitrogen(MNE)and intercropping mulberry without nitrogen(M0).Intercropping reduced both H’and D but nitrogen application showed no effect on diversity of microbial communities in alfalfa.There were obvious differences in using the six types of carbon sources between mulberry and alfalfa treatments.Nitrogen and intercropping increased the numbers of sole carbon substrate in mulberry treatments where the relative use rate exceeded 4%.While the numbers declined in alfalfa with nitrogen and intercropping.RDA indicated that URE was positive when intercropping mulberry was treated with nitrogen,but was negative in monoculture alfalfa treated with nitrogen.Soil pH and SWC were positive with mulberry treatments but were negat展开更多
A scheme for all-fiber clock enhancement of non-return-to-zero (NRZ) data based on cross-phase modulation (XPM) effect in nonlinear fibers is proposed and demonstrated in simulation. The simulation results indicate th...A scheme for all-fiber clock enhancement of non-return-to-zero (NRZ) data based on cross-phase modulation (XPM) effect in nonlinear fibers is proposed and demonstrated in simulation. The simulation results indicate that the clock-to-data ratio of NRZ signals at 64 Gb/s can be increased to 22.94 dB by using this scheme, and the pattern effect in clock enhanced signals is very weak. The ability of high speed operation up to 140 Gb/s of this scheme is also proved in our simulation.展开更多
Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the qu...Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the quality of clothing patterns is very high on conventional design,the input time and output amount ratio is relative low for conventional design.In order to break through the bottleneck of conventional clothing patterns design,this paper proposes a novel way based on generative adversarial network(GAN)model for automatic clothing patterns generation,which not only reduces the dependence of experienced designer,but also improve the input-output ratio.Design/methodology/approach-In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details,this paper improves the conventional GAN model from two aspects:a multi-scales discriminators strategy is introduced to deal with the local texture details;and the selfattention mechanism is introduced to improve the global artistic perception.Therefore,the improved GAN called multi-scales self-attention improved generative adversarial network(MS-SA-GAN)model,which is used for high resolution clothing patterns generation.Findings-To verify the feasibility and effectiveness of the proposed MS-SA-GAN model,a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures,and a comparative experiment is conducted on our designed clothing patterns dataset.In experiments,we have adjusted different parameters of the proposed MS-SA-GAN model,and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/value-Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GANmodel are superior to the conventional algorithms in some local texture detail indexes.In addition,a group of clothing design professionals is invited to evaluate the global artistic perception through a valencearousal scale.The scale results ha展开更多
基金supported by the Key R&D Program of Shaanxi Province,China(Grant Nos.2022GY-274,2023-YBSF-505)the National Natural Science Foundation of China(Grant No.62273273).
文摘Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.
基金the Heilongjiang Province Science Foundation for Youths(Grant No.QC2016018)the National Natural Science Foundation of China(Grant No.31600508)+2 种基金the Fundamental Research Funds for the Central University(2572017CA21)the Application Technology Research and Development Projects of Heilongjiang Province(Grant No.WB13B104)the Science and Technology Project of Heilongjiang Farms&Land Reclamation Administration(Grant No.HNK135-01-056)。
文摘Intercropping of mulberry(Morus alba L.)and alfalfa(Medicago sativa L.)is a new forestry-grass compound model in China,which can provide high forage yields with high protein.Nitrogen application is one of the important factors determining the production and quality of this system.To elucidate the advantages of intercropping and nitrogen application,we analyzed the changes of physicochemical properties,enzyme activities,and microbial communities in the rhizosphere soil.We used principal components analysis(PCA)and redundancy discriminators analysis to clarify the relationships among treatments and between treatments and environmental factors,respectively.The results showed that nitrogen application significantly increased pH value,available nitrogen content,soil water content(SWC),and urea(URE)activity in rhizosphere soil of monoculture mulberry.In contrast,intercropping and intercropping+N significantly decreased pH and SWC in mulberry treatments.Nitrogen,intercropping and intercropping+N sharply reduced soil organic matter content and SWC in alfalfa treatments.Nitrogen,intercropping,and intercropping+N increased the values of McIntosh diversity(U),Simpson diversity(D),and Shannon-Weaver diversity(H’)in mulberry treatments.However,PC A scatter plots showed clustering of monoculture mulberry with nitrogen(MNE)and intercropping mulberry without nitrogen(M0).Intercropping reduced both H’and D but nitrogen application showed no effect on diversity of microbial communities in alfalfa.There were obvious differences in using the six types of carbon sources between mulberry and alfalfa treatments.Nitrogen and intercropping increased the numbers of sole carbon substrate in mulberry treatments where the relative use rate exceeded 4%.While the numbers declined in alfalfa with nitrogen and intercropping.RDA indicated that URE was positive when intercropping mulberry was treated with nitrogen,but was negative in monoculture alfalfa treated with nitrogen.Soil pH and SWC were positive with mulberry treatments but were negat
文摘A scheme for all-fiber clock enhancement of non-return-to-zero (NRZ) data based on cross-phase modulation (XPM) effect in nonlinear fibers is proposed and demonstrated in simulation. The simulation results indicate that the clock-to-data ratio of NRZ signals at 64 Gb/s can be increased to 22.94 dB by using this scheme, and the pattern effect in clock enhanced signals is very weak. The ability of high speed operation up to 140 Gb/s of this scheme is also proved in our simulation.
基金This paper is supported by university fund project of Hubei Institute of Fine Arts,named“The construction of blended teaching mode based on flipped classroom-Taking the Course of“Fashion Painting Illustration”as an Example.”(No.202028)。
文摘Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the quality of clothing patterns is very high on conventional design,the input time and output amount ratio is relative low for conventional design.In order to break through the bottleneck of conventional clothing patterns design,this paper proposes a novel way based on generative adversarial network(GAN)model for automatic clothing patterns generation,which not only reduces the dependence of experienced designer,but also improve the input-output ratio.Design/methodology/approach-In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details,this paper improves the conventional GAN model from two aspects:a multi-scales discriminators strategy is introduced to deal with the local texture details;and the selfattention mechanism is introduced to improve the global artistic perception.Therefore,the improved GAN called multi-scales self-attention improved generative adversarial network(MS-SA-GAN)model,which is used for high resolution clothing patterns generation.Findings-To verify the feasibility and effectiveness of the proposed MS-SA-GAN model,a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures,and a comparative experiment is conducted on our designed clothing patterns dataset.In experiments,we have adjusted different parameters of the proposed MS-SA-GAN model,and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/value-Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GANmodel are superior to the conventional algorithms in some local texture detail indexes.In addition,a group of clothing design professionals is invited to evaluate the global artistic perception through a valencearousal scale.The scale results ha
