Research on the Optimization Algorithm of Computer Vision Technology Based on Deep Learning

This paper proposes an optimization algorithm for cross-cultural communication of static images based on computer vision. The algorithm combines deep learning with cultural perception, uses CNN to extract the visual features of images, and combines cultural understanding models to optimize elements such as color, composition and symbols in images to enhance the adaptability of images in different cultural backgrounds. We need to innovatively introduce adaptive optimization strategies to dynamically adjust the visual effects of images so that they can better convey information and resonate in different cultural environments. This paper verifies the effectiveness of the algorithm through experimental simulation. The experimental results show that compared with traditional image optimization methods, the optimization algorithm based on deep learning has significantly improved performance in cross-cultural communication. Specifically, the optimized images have improved in cultural adaptability, user understanding and visual effects. The cultural adaptability has increased by 20%, the user understanding has increased by 15%, and the image quality has increased by 10%. This study provides an innovative solution for the optimization of static images in cross-cultural communication, and has broad application prospects, especially in the fields of advertising design and international cultural exchanges, which can effectively improve the communication effect of images and the efficiency of cultural information transmission.