Arbitrary style transformation algorithm based on multi-scale fusion and compressed attention in art and design

Abstract

In art and design, style conversion algorithms can fuse the content of one image with the style of another image, thereby generating images with new artistic styles. However, traditional style conversion algorithms suffer from high computational complexity and loss of details during row image conversion. Therefore, this study introduces VGG16 multi-scale fusion feature extraction in any style transition algorithm and introduces a compressed attention mechanism to improve its computational complexity. Then it designs an arbitrary style transformation algorithm on the ground of multi-scale fusion and compressed attention. The results showed that the designed algorithm took 0.014 s and 0.021 s to process tasks on the COCO Stuff dataset and WikiArt dataset, respectively, proving its high computational efficiency. The loss values of the designed algorithm are 0.046 and 0.052, respectively, indicating strong fitting performance and good generalization ability. The IS score and FID score of the design algorithm are 2.36 and 91.67, respectively, proving that the generated images have high diversity and quality. The above results demonstrate the effectiveness and practicality of design algorithms in art and design. It has important theoretical and practical value in promoting the development of style conversion technology, enhancing the creativity and expressiveness of art and design.