Lacquerware Color and Pattern Design Method Based on Improved Clustering and Edge Detection
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To realize accurate extraction and digital design of lacquerware color and decorative patterns for traditional craft inheritance and innovation, this study proposes an improved technical method. The improved K-means++ clustering algorithm is used for color extraction. First, singular value decomposition (SVD) reduces the dimensionality of compressed images to retain core color information. Then, a quadratic clustering strategy optimizes the initial centers. For pattern detection, hybrid adaptive median filtering and bootstrap filtering denoise images, combined with adaptive linear interpolation suppression, improve edge positioning accuracy. Experiments on typical lacquerware images showed that when extracting 16 feature colors, the improved K-means++ had a mean peak signal-to-noise ratio (PSNR) of 31.47 dB and a structural similarity index (SSIM) of 0.95. When extracting 8 colors, it still maintained a PSNR of 28.83 dB. The improved Canny algorithm achieved a PSNR of 24.43 dB at 1.0% noise level, with 64.88% sensitivity and 93.04% specificity. It accurately restored color proportions of crafts like needle carving with a pulling knife and generates patterns with clear edges. This method synergistically optimizes color and edge extraction, enhances digital design precision and efficiency, and provides reliable support for traditional craft digital preservation.
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