## 2 Method
### Network Structure  
To enhance the quality of super-resolution images, we designed an innovative dual-style network, DSpix2 pix, based on the high-quality image generation capabilities of the StyleGAN-v2 framework. This network utilizes downsampling and StyleGAN-v2's style convolution layers to encode low-resolution images into constant vectors, enabling precise control over the content of the generated images. Additionally, during the decoding phase, it incorporates style information extracted from example images to enhance the diversity and controllability of the generated results. The loss structure adopts the pix2 pix loss framework [29], combining generative and adversarial losses to further optimize the detail quality and visual consistency of the images, achieving dual optimization of both content and style. The overall network structure is shown in Figure 1.  
The network consists of two parts: the generator and the discriminator. The discriminator is a residual network with 18 layers (resnet-18) [30]. The generator consists of three parts: two style structures and a generator structure. The generator generates high-resolution images, while Style 1 and Style 2 ensure that the generated images are as consistent as possible with the high-resolution ground truth in both content and style, especially during the generation of high-quality images, such as those at a 1024 \(\times\) 1024 resolution. Style 1, derived from StyleGAN-v2, is used to learn the style information of the training remote sensing image dataset. The mapping network, as a core component, consists of multiple fully connected layers and is used to transform the latent vector z (usually random noise) into style vector space \(w+\). This network can fully decouple the image style features, allowing the style vector in the \(w+\) space to independently control the styles of generated images at different layers [25]. Style 2 is a trained autoencoder (AE) [31], with its decoder layer design identical to that of the generator's decoder and super layers. By compressing and reconstructing high-resolution data, the features of the high-resolution image decoder layer are obtained. Subsequently, we input a high-resolution image into Style 2, and after inference, we extracted the Gram matrix [32] (style) from the features output by the Style 2 decoder layer. This matrix was then injected into the generator's decoder layer to continuously control the style of the generated images. The high-resolution image serves as a reference image, which can be any image from the training dataset, as long as it contains high-resolution style information. The generator consists of a network structure similar to U-Net [33]. The encoder of generator (G-Encoder) provides the content and style information of the low-resolution image. Since the style of the low-resolution image is similar to that of the high-resolution image, it offers strong control, reducing the difficulty of training the network and the size of the training dataset required. This is the foundation that enables the network to stably generate remote sensing images. The extra residual blocks in the generator (G-Extra) are set to enhance the network's expressive ability, with the number of residual blocks determined based on the complexity of the task. In the experiments conducted in this paper, two blocks are set. The decoder of the  
Figure 1: Overall network framework structure of DSpix2 pix. generator (G-Decoder) restores the feature maps obtained from the encoder to their original size, generating images that contain more high-resolution information but retain the same size as the low-resolution image. During this process, the decoder continuously extracts style information from Style 1, Style 2, and the low-resolution image. For example, if the input low-resolution image has a size of 256 \(\times\) 256, the high-resolution image generated by the decoder will also have a size of 256 \(\times\) 256, but with more details.