# High-Quality Text-to-Image Generation Using High-Detail Feature-Preserving Network
## 4 Experiments
### Comparisons with the State-of-the-Art Algorithms  
To validate the performance of the proposed method, HDFpNet was compared with state-of-the-art approaches on the CUB-Bird and MS-COCO datasets. The FID and R-precision results are presented in Tables 1 and 2, respectively. As shown in Table 1, HDFpNet outperformed the compared methods in terms of the FID, with lower FID scores being indicative of better performance. For example, on the CUB-Bird dataset, HDFpNet and DM-GAN achieved FID scores of 13.89 and 16.09, respectively, while, on the MS-COCO dataset, their respective FID scores were 27.21 and 32.64. The lower FID scores achieved by HDFpNet demonstrate that it obtains a superior data distribution. Table 2 also reveals that the proposed HDFpNet model achieved R-precision scores of 84.33% and 92.44% on the CUB-Bird and MS-COCO datasets, respectively. These scores are notably higher than those achieved by the other methods. The high R-precision scores indicate that the proposed model generated images that closely matched their corresponding descriptions, thereby verifying its effectiveness. Figure 3 presents examples of images generated by HDFpNet and the state-of-the-art models. Overall, the HDFpNet model generated images with greater detail than the StackGAN [9], AttnGAN [10], and DM-GAN [14] models. This superior performance can be attributed to the fact that HDFpNet retained the image features selected by DM-GAN, enabling it to better understand the logic of the input description and present a clearer image structure. An analysis of the image samples generated from the CUB-Bird dataset (Figure 3) indicates that, at the word level, the AttnGAN [10] and DM-GAN [14] models performed well in capturing and presenting text features. However, HDFpNet was even more effective in displaying word-level features, generating images that accurately reproduced object features and structures in detail. These images closely matched the corresponding input text and contained fine details such as the feather textures and other anatomical characteristics (e.g., eyes and legs) of birds (Figure 3). As a result, the generated bird images closely resembled real images. These findings confirm that the FESFE module enables the HDFpNet model to generate more realistic images. Furthermore, the MS-COCO dataset was used to test the network's ability to generate images containing multiple objects (Figure 4). When an input text described multiple themes or objects with complex structures, generating an accurate image was challenging. In each image, HDFpNet could identify the most essential themes and capture the main scene, with the remaining textual content being sorted in a logical sequence to arrange the objects in the image. For instance, as shown in the second row of Figure 4, HDFpNet could accurately generate an image of a room with wooden textural characteristics. These results further verify that the proposed model can enhance image details and follow textual logic. Figure 5 presents comparisons of real images with those generated by the HDFpNet model at various image enhancement stages. The generated images were based on the feature maps created by the iT2 IG module using the CUB-Bird dataset. The results reveal that, even before the final enhancement stage, the generated images already contained all of the required details. The final enhancement stage further improved these details to create more realistic images. The feature maps enhanced by the FFE block and CA mechanism facilitated the generation of objects with accurate structures and colors. The feature enhancement process at each stage involved optimizing the feature map generated in the previous stage. This process significantly improved the object structures and details, such as eyes and texture features, resulting in generated images that closely resembled real images. Figure 6 presents various types of images generated by HDFpNet using the same text descriptions from the CUB-Bird dataset. Despite being generated from the same