Recently, deep learning technique has been widely employed to deal with face super-resolution (FSR) problem. It aims to predict the nonlinear relationship between the low-resolution (LR) face images and corresponding high-resolution (HR) ones, which could recover the high-frequency details from the LR degraded textures. However, either CNN-based or Transformer-based approaches mostly enhance the details by exploiting the relationship of local pixels or patches on LR features, the nonlocal features are not fully taken into account for producing high-frequency textures. To improve the above problem, we design a novel dual-branch module which consists of Transformer and CNN respectively. The Transformer branch extracts multiple scale feature embeddings and explores local and nonlocal self-attention simultaneously. Thus, the parallel self-attention mechanism has superior capabilities to capture the local and nonlocal dependencies on face image in the face reconstruction. Furthermore, the traditional CNNs usually extract features by combining pixels in a local convolutional kernel, it may be not effective to recover lost high-frequency details since the variations of local pixels are not well measured, which is important in recovering vivid edges and contours. To this end, we propose the local variation based attention block on the CNN branch, which could enhance the capabilities by directly extracting features from the variation of neighboring pixels. Finally, the Transformer-branch and CNN-branch are combined together by the modulation block to fuse both nonlocal and local advantages from two branches. Experimental results demonstrate the effectiveness of the proposed method when compared with state-of-the-art approaches.

Recently, Transformer-based architecture has been introduced into face super-resolution task due to its advantage in capturing long-range dependencies. However, these approaches tend to integrate global information in a large searching region, which neglect to focus on the most relevant information and induce blurry effect by the irrelevant textures. Some improved methods simply constrain self-attention in a local window to suppress the useless information. But it also limits the capability of recovering high-frequency details when flat areas dominate the local searching window. To improve the above issues, we propose a novel self-refinement mechanism which could adaptively achieve texture-aware reconstruction in a coarse-to-fine procedure. Generally, the primary self-attention is first conducted to reconstruct the coarse-grained textures and detect the fine-grained regions required further compensation. Then, region selection attention is performed to refine the textures on these key regions. Since self-attention considers the channel information on tokens equally, we employ a dual-branch feature integration module to privilege the important channels in feature extraction. Furthermore, we design the wavelet fusion module which integrate shallow-layer structure and deep-layer detailed feature to recover realistic face images in frequency domain. Extensive experiments demonstrate the effectiveness on a variety of datasets.