## 2 Related Work  
### Face Super-Resolution  
Deep neural networks have revolutionized FSR tasks by achieving remarkable advancements. Face super-resolution methods can be broadly categorized into two groups based on the utilization of facial priori information.  
The first category comprises methods that leverage facial prior information. [PERSON] et al. [13] introduced a two-branch multitasking network that utilizes underlying and intermediate feature information to enforce constraints on the facial components of LR faces, leading to improved preservation of the complete facial structure. [PERSON] et al. [12] proposed FSRNet, which achieves impressive results in SR of very low-resolution faces by incorporating facial landmark heat maps into the feature map to resolve facial features, it aims to concentrate on the localization of facial signs, but it does not adequately consider the region around the sign's facial attributes. [PERSON] et al. [14] devised a face attention loss based on facial landmark heat maps and employed a progressive training method for face reconstruction, while the process of extracting heat maps of facial landmarks greatly increases the training process. In order to restore fundamental facial features without distortion, [PERSON] et al. [15] proposed an iterative collaboration approach that employs facial priors generated by their face keypoint recovery network to assist in FSR. However, the multi-stage iterative process also amplifies errors due to incorrect priors. To obtain SR images at arbitrary scales, [PERSON] et al. [11] incorporated identity prior into the reconstruction process and employed multiple models for progressive cascade reconstruction of faces, but the cascading structure also makes the network larger In order to fully capture the potential of prior information and multi-scale information, [PERSON] el al. [20] proposed a two-stage network, a ParsingNet is used to extract parsing map, which is then combined with LR image as input to the reconstruction network, crucial facial details and contours are restored by integrating multi-stage features. While utilizing additional facial prior information enhances network performance, it necessitates extra data annotation efforts, and the challenge of facial structure deformation arising from inaccurate face prior information persists.  
The second category encompasses methods that do not rely on facial a priori information. In order to obtain sharper facial details, [PERSON] et al. [18] introduced a method featuring a two-branch sub-network, where one branch focuses on global constraints to reconstruct face images while the other branch enhances local facial details. [PERSON] et al. [16] proposed a novel spatial residual attention network that employs facial attention units to prioritize the recovery of important facial structures, thereby enhancing the network's representation capability, while it lacks inter-channel correlation interaction. To fully leverage the facial attribute information. [PERSON] et al. [19] presented a facial attribute capsule network that transforms extracted facial feature maps into facial attribute capsules to obtain a complete facial structure, leveraging semantic and probabilistic information to generate corresponding high-resolution faces. To restore facial fine details and textures, [PERSON] et al. [17] incorporated wavelet prediction into their network to predict missing wavelet details of facial images, resulting in finer face reconstructions. However, the incorporation of wavelet prediction also increases the training overhead of the network. These methods demonstrate the effectiveness of alternative strategies that exploit network architectures and attention mechanisms to recover facial details without relying on explicit facial prior information.