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Research on Multi-scale Geometric Analysis and Compositional Optimization Methods in the Creation of Haikai Ink Figure Painting

Momo Feng

Momo Feng

Shanghai Academy of Fine Arts, Shanghai UniversityShanghai, China
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Feng, Momo
 und 
Ying Bai

Ying Bai

Shanghai Academy of Fine Arts, Shanghai UniversityShanghai, China
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Bai, Ying
  
24. März 2025
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Online veröffentlicht: 24. März 2025
Eingereicht: 14. Nov. 2024
Akzeptiert: 27. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0787
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© 2025 Momo Feng et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The picture of water ink is like the objective evaluation of fusion performance
The picture of water ink is like the objective evaluation of fusion performance

Figure 2.

Lena and Barbara reconstructed image performance under three sparse bases
Lena and Barbara reconstructed image performance under three sparse bases

The LP filter selects the experimental results

Image 9-7 Pkva 5-3 Burt
SNR/dB PSNR/dB SNR/dB PSNR/dB SNR/dB PSNR/dB SNR/dB PSNR/dB
Barbara 14.2 75.4 13.1 74.5 13.5 75.2 13.2 74.2
Peppers 18.3 79.1 15.4 77.3 18.1 78.4 17.4 78.5
Zoneplate 4.1 61.2 2.3 59.1 3.6 60.5 3.5 60.1
Mandril_gray 9.0 73.7 7.2 71.3 8.4 73.2 8.1 73.1
Lena 17.4 80.5 17.3 77.3 15.4 80.1 16.1 78.6

Multi-focus fusion image performance objective evaluation

Method Entropy joint-entropy MI edge-intensity AG
Wavelet pixel-based 8.965 11.213 5.231 55.124 5.321
Wavelet window-based 9.123 12.005 6.554 71.019 6.659
Contourlet pixel-based 9.047 12.396 5.703 66.278 7.113
Contourlet window-based 9.258 12.347 6.445 70.124 7.536
Algorithm of this article 9.461 12.565 6.857 75.313 7.789

The evaluation properties of the optimized water and ink objects were scored

Painting label Saturation degree ES Contrast ratio EC Clarity ER Composition EM
Original painting 1 0.8563 0.2365 0.3659 0.7869
Optimized painting 1 0.9224 0.8024 0.7551 0.8334
Original painting 2 0.1635 0.7545 0.9582 0.6543
Optimized painting 2 0.8759 0.8213 0.9966 0.7169
Original painting 3 0.1025 0.6569 0.2874 0.7585
Optimized painting 3 0.5964 0.7254 0.8531 0.9367
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