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Digital printing image generation method based on style transfer.

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Title: Digital printing image generation method based on style transfer.
Authors: Su, Zebin1,2,3 (AUTHOR), Zhao, Siyuan2,3 (AUTHOR), Zhang, Huanhuan2,3 (AUTHOR), Li, PengFei2 (AUTHOR) li6208@163.com, Lu, Yanjun1 (AUTHOR)
Source: Textile Research Journal. Dec2023, Vol. 93 Issue 23/24, p5211-5223. 13p.
Subjects: Digital printing, Image registration, Image compression, Textile printing, Problem solving
Abstract: Digital printing has been widely used in textile printing production. In the process of designing digital printing patterns, an image generative model is needed to assist in obtaining more diversified patterns. However, the current model involves large storage space and high computing cost, which affects the promotion of digital printing customized production. To solve the problem, this article proposes a digital printing image generation method based on style transfer. Firstly, a style transfer method based on exact feature distribution matching is constructed to realize the accurate matching from image content to style features. And a balanced loss function is used to enhance the universality of the proposed method. Furthermore, knowledge distillation is introduced to compress the method proposed to reduce the hardware requirements when processing high-resolution digital printing images. Finally, a segmented training strategy is proposed to solve the performance degradation caused by model compression. The experimental results show that when processing images with a resolution of 3000 × 3000, the storage capacity of the model is only 2.68 MB and only 0.20 TFLOPs is required. The maximum processing resolution is more than 8K. The pattern obtained by this model is of high quality and can meet the needs of digital printing production. [ABSTRACT FROM AUTHOR]
Copyright of Textile Research Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Digital printing image generation method based on style transfer.
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  Data: <searchLink fieldCode="JN" term="%22Textile+Research+Journal%22">Textile Research Journal</searchLink>. Dec2023, Vol. 93 Issue 23/24, p5211-5223. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+printing%22">Digital printing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink><br /><searchLink fieldCode="DE" term="%22Textile+printing%22">Textile printing</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Digital printing has been widely used in textile printing production. In the process of designing digital printing patterns, an image generative model is needed to assist in obtaining more diversified patterns. However, the current model involves large storage space and high computing cost, which affects the promotion of digital printing customized production. To solve the problem, this article proposes a digital printing image generation method based on style transfer. Firstly, a style transfer method based on exact feature distribution matching is constructed to realize the accurate matching from image content to style features. And a balanced loss function is used to enhance the universality of the proposed method. Furthermore, knowledge distillation is introduced to compress the method proposed to reduce the hardware requirements when processing high-resolution digital printing images. Finally, a segmented training strategy is proposed to solve the performance degradation caused by model compression. The experimental results show that when processing images with a resolution of 3000 × 3000, the storage capacity of the model is only 2.68 MB and only 0.20 TFLOPs is required. The maximum processing resolution is more than 8K. The pattern obtained by this model is of high quality and can meet the needs of digital printing production. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Textile Research Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/00405175231195367
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 5211
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      – SubjectFull: Digital printing
        Type: general
      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Image compression
        Type: general
      – SubjectFull: Textile printing
        Type: general
      – SubjectFull: Problem solving
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      – TitleFull: Digital printing image generation method based on style transfer.
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            NameFull: Su, Zebin
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            NameFull: Zhao, Siyuan
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            NameFull: Zhang, Huanhuan
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            NameFull: Li, PengFei
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            NameFull: Lu, Yanjun
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            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
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              Value: 93
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              Value: 23/24
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            – TitleFull: Textile Research Journal
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