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A Hierarchical Smoothing Method for Animation Image Based on Scale Decomposition

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Advanced Hybrid Information Processing (ADHIP 2023)

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Abstract

In order to solve the problem of low quality of animated images affected by noise, a hierarchical smoothing method for animated images based on scale decomposition is proposed. Get the animation base image, and obtain the detail layer of the source image and target image. Use U-net convolutional neural network to select the decomposition box, select the results according to the remote sensing image segmentation box, and design the image decomposition process. Adjust the animation decomposition scale, focus on measuring multi-scale morphology, use the mean coordinate method to fuse the brightness of the target image, and retain rich details of the image. The fusion image smooth mosaic processing flow is designed, and the minimum variance standard is used to obtain the best matching combination. The gradient is used to represent the direction and size of the pixel changes in the animation image, and the details of the animation image are enhanced by means of superposition correction to achieve image edge smoothing. The experimental results show that the image details obtained by this method are consistent with the image samples, the signal-to-noise ratio is above 90 dB, and the longest smoothing processing time is 27 s, which can obtain high-quality animation images.

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References

  1. Gao, C., Song, C., Zhang, Y., et al.: Improving the performance of infrared and visible image fusion based on latent low-rank representation nested with rolling guided image filtering. IEEE Access 16(5), 1–14 (2021)

    Google Scholar 

  2. Chen, H., Deng, L., Qu, Z., et al.: Tensor train decomposition for solving large-scale linear equations. Neurocomputing 464, 203–217 (2021)

    Article  Google Scholar 

  3. Chew, A., Ji, A., Zhang, L.: Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism. Autom. Constr. 133(01), 1–19 (2022)

    Google Scholar 

  4. Zhang, X., Yan, H.: Medical image fusion and noise suppression with fractional-order total variation and multi-scale decomposition. IET Image Proc. 15(8), 1688–1701 (2021)

    Article  Google Scholar 

  5. Ren, L., Pan, Z., Cao, J., et al.: Infrared and visible image fusion based on edge-preserving guided filter and infrared feature decomposition. Signal Process. 186(2), 108–116 (2021)

    Google Scholar 

  6. Chen, J., Wu, K., Cheng, Z., et al.: A saliency-based multiscale approach for infrared and visible image fusion. Signal Process. 182(4), 107–118 (2021)

    Google Scholar 

  7. Yanxia, Y.: Detail blur enhancement method of 3D animation image based on optical parametric magnification. Laser J. 43(09), 114–118 (2022)

    Google Scholar 

  8. Li, K., Zhang, J.: Multi-layer encoding and decoding model for image captioning based on attention mechanism. J. Comput. Appl. 41(09), 2504–2509 (2021)

    Google Scholar 

  9. Yali, Q., Jicai, M., Hongliang, R., et al.: Image reconstruction based on Gaussian smooth compressed sensing fractional order total variation algorithm. J. Electron. Inf. Technol. 43(07), 2105–2112 (2021)

    Google Scholar 

  10. Madureira, A.: Hybrid localized spectral decomposition for multiscale problems. SIAM J. Numer. Anal. 59(2), 829–863 (2021)

    Article  MathSciNet  Google Scholar 

  11. Wang, K., Wang, S., Sun, Q., Liu, C., Chen, S.: Point cloud segmentation matching for 3d reconstruction using multi-layer lidar. J. Changchun Univ. Sci. Technol. 43(04), 49–56 (2020)

    Google Scholar 

  12. Li, Y., Wang, J.: Edge feature extraction method of Brillouin scattering spectral image. Opt. Commun. Technol. 45(03), 37–41 (2021)

    Google Scholar 

  13. He, L., Su, L., Zhou, G., Yuan, P., Lu, B., Yu, J.: Image super-resolution reconstruction based on multi-scale residual aggregation feature network. Laser Optoelectron. Progress 58(24), 250–259 (2021)

    Google Scholar 

  14. Li, C. Research on optimization of 3D image enhancement based on adaptive. Comput. Simul. 37(12), 358–361 (2020)

    Google Scholar 

  15. Liu, W., et al.: Research on intelligent image processing based on deep learning. Autom. Instrument. 12(08), 60–63 (2020)

    Google Scholar 

  16. Zhao, S., Yang, T.: A coherent coefficient based filter of complex number images. Comput. Technol. Develop. 30(02), 7–11 (2020)

    Google Scholar 

  17. Dr. Neetu, A.: Image recognition through human eyes, computers and artificial intelligence. J. Res. Sci. Eng. 3(3), 132–141 (2021)

    Google Scholar 

  18. Duan, H., Wang, Z., Wang, Y.: Two-channel saliency object recognition algorithm based on improved YOLO network. Laser Infrared 50(11), 1370–1378 (2020)

    Google Scholar 

  19. Ajay, R., et al.: Computer vision and machine learning for image recognition: A review of the convolutional neural network (CNN) model. Asian J. Multidimen. Res. 10(10), 1023–1029 (2021)

    Google Scholar 

  20. Jin, H., Cao, T., Xiao, C., Xiao, Z.: Video summary generation based on multi-feature image and visual saliency. J. Beijing Univ. Aeron. Astron. 47(03), 441–450 (2021)

    Google Scholar 

  21. Jeya, C.A., Dhanalakshmi, K.: Content-based image recognition and tagging by deep learning methods. Wireless Pers. Commun. 123(1), 813–838 (2021)

    Article  Google Scholar 

  22. Xiang, J., Xv, H.: Research on image semantic segmentation algorithm based on deep learning. Appl. Res. Comput. 37(S2), 316–317+3 (2020)

    Google Scholar 

  23. Li, M., Li, L., Lei, S.: Application of unsupervised fuzzy clustering algorithm in image recognition. Techn. Autom. Appl. 39(01), 121–124+159 (2020)

    Google Scholar 

  24. Li, P., Li, J., Wu, L., Hu, J.: Image recognition algorithm based on threshold segmentation method and convolutional neural network. J. Jilin Univ.(Science Edition) 58(06), 1436–1442 (2020)

    Google Scholar 

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Authors and Affiliations

  1. Guangzhou Academy of Fine Arts, Guangzhou, 510006, China

    Jieling Jiang & Wei Li

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  1. Jieling Jiang
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  2. Wei Li
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Correspondence to Jieling Jiang .

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Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Lin Yun

  2. Harbin Engineering University, Harbin, China

    Jiang Han

  3. Harbin Engineering University, Harbin, China

    Yu Han

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jiang, J., Li, W. (2024). A Hierarchical Smoothing Method for Animation Image Based on Scale Decomposition. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-031-50549-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-50549-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50548-5

  • Online ISBN: 978-3-031-50549-2

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