| Title |
Flat Color Design Integrating Image Color Reconstruction Algorithm and Fabric Color Extraction |
| Authors |
(Jifeng Zhong) ; (Yongguang Wei) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.6.741 |
| Keywords |
Density peak clustering; Color reconstruction; Peak signal-to-noise ratio; Structural similarity; ; Feature extraction; Color design |
| Abstract |
The development of multimedia technology leads to an increasing demand for diversified graphic color design. A fusion image reconstruction algorithm for fabric color extraction is proposed by combining fabric texture features. The study first extracts fabric features, then generates a matching color table, and reconstructs the image colors based on the color table. The proposed image color reconstruction algorithm is tested. After multiple iterations, the density peak clustering gradually converged to 0.36 on the test set. The density peak clustering algorithm had a high average pixel accuracy of 94.9% after multiple iterations, indicating a high pixel accuracy. The mean intersection over the union of the density peak clustering algorithm reached 91.1%, significantly higher than other methods.
This proposed image reconstruction algorithm was compared with different methods. For the mean absolute error and mean square error, this method had the lowest values, which were 8.915 and 7.224, respectively. Its regression effect was good and the error was small. The proposed algorithm had the highest numerical value, with peak signal-to-noise ratio and structural similarity values of 0.875 and 28.733, respectively. It can accurately achieve image color reconstruction and then carry out planar color design. |