A Visual Communication Optimization Method for Human-Computer Interaction Interfaces Using Fuzzy Logic and Wavelet Transform

This paper proposes a visual communication optimization method for human-computer interaction interfaces based on fuzzy logic and wavelet transform, aiming to solve the problems of blurred information, unclear edge details, and noise interference in interface visual communication. First, fuzzy logic is used to quantify fuzzy areas and uncertain information in the image, and fuzzy clustering methods are used to achieve precise positioning and feature enhancement of key areas; then, wavelet transform is used to decompose the image into multiple scales to effectively separate low-frequency areas. The overall structural information and edge detail features of high-frequency areas highlight key visual elements in the interface. The optimized human-computer interaction interface has clearer edge contours, effectively suppressed noise, and more accurate expression of visual information. In the experimental part, edge detection, noise suppression evaluation, multi-scale visual detail retention, and user satisfaction comparison experiments were conducted. The results show that after fuzzy logic and wavelet transform processing, the peak signal-to-noise ratio (PSNR) of the interface image is significantly improved, the edge clarity is significantly improved at different scales, and the user satisfaction level is “satisfied” and “very satisfied” The rating ratio has increased significantly. This method shows superiority in terms of visual communication quality, information accuracy, and user experience, and has strong practicability and stability. In the future, this method can be further combined with deep learning, personalized recommendations and virtual reality technology to promote the intelligent and efficient development of humancomputer interaction interfaces in more fields.