3D animation design image detail enhancement based on intelligent fuzzy algorithm

When zooming in on low resolution images, Lanczos interpolation method is prone to produce ringing effects at the edges and high contrast areas. When processing high texture 3D animations, the method cannot effectively optimize for different areas, significantly affecting image quality and detail representation. This study utilized SRGAN (Super-Resolution Generative Adversarial Network) to enhance image resolution details, combined with fuzzy logic and attention mechanism, adaptively focused on different regions of the image, enhanced key details and suppressed noise. The image was divided into superpixel regions using SLIC (Simple Linear Iterative Clustering) algorithm, and local features such as texture, contrast, and edge intensity were extracted; in the SRGAN model, the generator improved image resolution through deep residual blocks and Convolutional Neural Network (CNN), while the discriminator optimized the generated image quality through adversarial training; at the same time, a Fuzzy Logic System (FLS) was constructed to dynamically adjust the image fuzzy degree; channel and spatial attention modules in the generator were integrated to enhance key area details. The research results indicated that Fuzzy Algorithm-SRGAN (FA-SRGAN) had an average PSNR (Peak Signal-to-Noise Ratio) exceeding 32.8 dB in four test scenes; in architectural design scenes, the algorithm improved image contrast by 18%, and increased energy and uniformity by 14% and 11%, respectively. The adopted approach can significantly enhance the details of different regions in high texture 3D animation design images.