Automatic Generation and Optimization Design of Embroidery Patterns Based on Image Processing

With the improvement of living environment and the improvement of people's quality of life, embroidery, as a traditional handicraft art, is gradually receiving more attention and love from people. Deep learning (DL) has demonstrated strong capabilities in image generation and processing, particularly in generating visually reasonable and highly detailed images. This provides new opportunities for the automatic generation and optimization design of embroidery patterns. Traditional embroidery pattern generation methods may rely on rules, templates, or manual adjustments, while DL methods can automatically learn complex patterns and structures in images by training large amounts of data, improving the efficiency of embroidery pattern generation and optimization. The DL model can process a large amount of data in a short period of time and automatically generate and optimize patterns. This article designs an automatic generation and optimization algorithm for embroidery patterns based on image processing, using DL technology to learn and generate innovative and artistic embroidery patterns from a large amount of embroidery image data. The experimental results show that the algorithm proposed in this paper can achieve automatic generation and optimization of embroidery patterns, meeting the needs of users for unique and beautiful embroidery patterns.