Exploration and Education in Red Culture Visual Design With Intelligent Image Technology

To address challenges in teaching red culture visual design, this study uses intelligent image processing, including denoising and modeling algorithms, to extract unique indicators from image data. A theoretical model captures visual changes, demonstrating improved accuracy by comparing original and optimized data. Gaussian parameters show linear then stable expansion, while variance highlights significant volatility. Tracking three matrix types reveals distinct trends. Optimized data exhibit better forms with reduced errors, enhancing teaching practices and improving calculation outcomes for red culture education.