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Dyeing creation: a textile pattern discovery and fabric image generation method

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Abstract

Creating different textile patterns to generate printable fabric images is a difficult image processing task. To accomplish this task, we propose a novel framework for dyeing creation, which allows non-professionals to design individual fabric images. The two main components of this framework are textile pattern discovery and fabric image generation. Since the objects in the fabric image are multi-category and multi-scale, we employ a combination of object pattern and template pattern to discover the repetitive pattern, which can better extract objects and analyze spatial structure. However, the image created with objects and templates cannot be dyed directly, because it does not meet the physical size requirements of dyeing. Therefore, we propose an image super-resolution method for fabric image generation based on edge information prior. It solves the high magnification problem of single image by using deep neural network without training data sets. Extensive experiments on fabric images demonstrate that the proposed algorithm achieves good results both qualitatively and quantitatively. Our method has comparable accuracy compared with state-of-the-art methods and visual results demonstrate our superiority in restoring edges while generating fabric images.

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References

  1. Assaf S, Nadav C, Michal I (2018) “zero-shot” super-resolution using deep internal learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3118–3126

  2. Bochkovskiy A, Chien YW, Hong YML (2020) Yolov4: Optimal speed and accuracy of object detection

  3. Cai Y, Baciu G (2013) Detecting, grouping, and structure inference for invariant repetitive patterns in images. IEEE Trans Image Process 22(6):2343–2355

    Article  MathSciNet  Google Scholar 

  4. Cai Y, Baciu G (2013) Translation symmetry detection: A repetitive pattern analysis approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 223–228

  5. Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Analy Mach Intell 38 (2):295–307

    Article  Google Scholar 

  6. Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65

    Article  Google Scholar 

  7. Girshick R, Donahue J, Trevor D, Jitendra M (2013) Rich feature hierarchies for accurate object detection and semantic segmentation

  8. Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: IEEE 12th international conference on computer vision, IEEE, pp 349–356, p 2009

  9. Gong K, Catana C, Qi J, Li Q (2018) Pet image reconstruction using deep image prior, IEEE Transactions on Medical Imaging

  10. Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5197–5206

  11. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646–1654

  12. Kim K, Se YC (2018) Sredgenet Edge enhanced single image super resolution using dense edge detection network and feature merge network. arXiv:1812.07174

  13. Lagae A, Dutre P (2005) A procedural object distribution function ACM transactions on graphics (TOG)

  14. Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 624–632

  15. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690

  16. Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527

    Article  Google Scholar 

  17. Liu J, Liu Y (2013) Grasp recurring patterns from a single view. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2003–2010

  18. Liu S, Ng T-T, Sunkavalli K, Do MN, Shechtman E, Carr N (2015) Patchmatch-based automatic lattice detection for near-regular textures. In: Proceedings of the IEEE international conference on computer vision, pp 181–189

  19. Liu Y, Lin W-C, Hays J (2004) Near-regular texture analysis and manipulation. ACM Trans Graph (TOG) 23:368–376. ACM

    Article  Google Scholar 

  20. Mastan ID, Raman S (2019) Multi-level encoder-decoder architectures for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 0–0

  21. Nazeri K, Ng E, Joseph T, Qureshi F, Ebrahimi M (2019) Edgeconnect: Generative image inpainting with adversarial edge learning. arXiv:1901.00212

  22. Park M, Brocklehurst K, Collins RT, Liu Y (2009) Deformed lattice detection in real-world images using mean-shift belief propagation. IEEE Trans Pattern Anal Mach Intell 31(10):1804–1816

    Article  Google Scholar 

  23. Redmon J, Divvala S, Girshick R, Ali F (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

  24. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  25. Rosenfeld A (1976) Digital picture processing. Academic Press, New York

    MATH  Google Scholar 

  26. Santoni C, Fabio P (2016) gtangle: a grammar for the procedural generation of tangle patterns. Acm Trans Graph 35(6cd):1–11

    Article  Google Scholar 

  27. Schindler G, Krishnamurthy P, Lublinerman R, Liu Y, Dellaert F (2008) Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In: 2008 IEEE Conference on computer vision and pattern recognition, IEEE, pp 1–7

  28. Spinello L, Triebel R, Vasquez D, Arras KO, Siegwart R (2010) Exploiting repetitive object patterns for model compression and completion. In: European conference on computer vision. Springer, pp 296–309

  29. Ulyanov D, Vedaldi A, Lempitsky V (2018) Deep image prior. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9446–9454

  30. Vector Magic: Convert JPG, PNG images to SVG, EPS, AI vectors. [EB/OL]. https://vectormagic.com/

  31. Zhang Y, Li K, Li K, Wang L, Zhong B, Yun F (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the european conference on computer vision (ECCV), pp 286–301

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Authors and Affiliations

  1. State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Shuang Wang & Zhengxing Sun

  2. Jiangsu Vocational Institute Of Commerce, Nanjing, 211168, China

    Shuang Wang

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  1. Shuang Wang
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  2. Zhengxing Sun
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Correspondence to Zhengxing Sun.

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Wang, S., Sun, Z. Dyeing creation: a textile pattern discovery and fabric image generation method. Multimed Tools Appl 80, 26511–26530 (2021). https://doi.org/10.1007/s11042-021-10902-3

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