Image Design Feature Enhancement Method Based on Transformer and Multimodal Neural Networks

With the rapid development of artificial intelligence technology, image design feature enhancement plays a crucial role in computer vision, image processing, and multimedia applications. Traditional feature enhancement methods often have limitations when dealing with complex and changeable image data. Therefore, the study proposes an innovative MN-T (Multimodal Neural Network enhanced by Transformer) strategy to overcome these challenges. The MN-T strategy combines the attention mechanism of Transformer with the cross-modal learning capabilities of multimodal neural networks to more accurately capture and enhance key features in images. The Transformer attention mechanism enables MN-T to efficiently process global and local information in images, while the cross-modal learning capability of multimodal neural networks further enhances its ability to understand and express image features. The results show that MN-T strategy has excellent performance in processing different image data. Compared with the existing image design feature enhancement methods, MN-T strategy has achieved significant improvement in the root mean square error (RMSE), mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and other key performance indicators. The research results of this paper not only provide a new idea and method for image design feature enhancement methods, but also provide a new theoretical support and experimental basis for the research of related fields.