## 2 Related Work  
### Face Super-Resolution  
With the advent of deep learning techniques [24; 25; 2], deep convolution neural networks (CNNs) have been making remarkable advancements in enhancing the quality of face images. One of the first CNN-based FSR methods was proposed by [PERSON] et al. [26], demonstrating significant improvements in FSR performance compared to traditional FSR methods. To delve deeper into the facial information, [PERSON] et al. [27] employed reinforcement learning to uncover the interdependent relationships among various facial components. [PERSON] et al. [9] introduced super-identity loss to help the network generate more accurately identified super-resolution face information. Unlike the FSR methods mentioned above that directly recover face images, [PERSON] et al. [28] introduced the wavelet transform to project face images into wavelet spaces for capturing rich contextual information. [PERSON] et al. [19] applied the Fourier transform to obtain an image-size receptive field to capture the global facial structures.  
Motivated by the remarkable achievements of generative adversarial networks (GANs) [29], [PERSON] et al. [30] developed a collaborative suppression and replenishment framework based on GANs. [PERSON] [31] claimed that feature maps with additional facial attribute information could significantly reduce the ambiguity in FSR and combined these supplement residual images with GANs. Noticing that GAN-based methods are computationally intensive, PCA-SRGAN [32] leveraged Principal Component Analysis decomposition, while SPGAN [33] adopted a supervised pixel-wise loss approach to facilitate the GAN training process.  
Noting that human faces are highly structured, several FSR methods utilized facial priors, including face landmarks and face parsing maps, to improve the reconstruction performance. [PERSON] et al. [34] introduced facial parsing maps to guide the end-to-end FSR convolution network. [PERSON] et al. [35] combined a well-designed heatmap loss with GANs to ensure the face structure consistency between high-resolution (HR) and super-resolved (SR) face images. [PERSON] et al. [36] proposed 3D facial priors for capturing sharp facial structures in face images with large pose variations. Due to the difficulty of directly estimating the prior from degraded LR facial images, DIC [37] developed an iterative process where FSR and prior estimation were performed repeatedly to enhance FSR performance. While the FSR models with facial priors have achieved promising outcomes, there is a problem that has not received sufficient attention. Specifically, the deconvolution or shuffle layers in the upsampling process neglect the relationship between adjacent pixels, which can break the highly structured face image prior, limiting the FSR performance.  
In recent years, the attention mechanism has emerged as a prominent approach in computer vision tasks [38; 39; 40; 41]. For instance, [PERSON] et al. [10] introduced a face attention unit to capture facial structures, while [PERSON] et al. [11] designed an external-internal split attention group to reconstruct more detailed facial images. Moreover, transformers, which have already demonstrated their effectiveness in various fields, are also widely applied in computer vision tasks such as image recognition [42; 43] and restoration [15; 17; 44]. The self-attention mechanism, which is the core of transformers, is promised to capture both long- and short-range correlations between words or pixels [45]. However, feeding feature maps directly into transformers without guidance will result in the loss of some fine-grained details, limiting facial structure reconstruction performance. Therefore, it is essential to design an effective guiding block to identify the crucial facial components for the transformers.