Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network
Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829579/ |
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| Summary: | Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which seriously affects the accuracy of color-separation algorithms. In addition, the traditional color-separation algorithm needs to rely on the manual setting of clustering parameters, which is very time-consuming and laborious. To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries; secondly, we carry out the initial clustering through the SOM neural network to simplify the image information; and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image; finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. The experimental results show that the algorithm proposed in this study can effectively deal with degraded pattern images, and the clustering effectiveness evaluation indices perform well. |
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| ISSN: | 2169-3536 |