Face Super-Resolution via Progressive-Scale Boosting Network

Deep-learning-based face super-resolution (FSR) algorithms have performed more than traditional algorithms. However, existing methods need to pass multi-scale priors effectively constrained models. To alleviate this problem, we propose a progressive-scale boosting network framework, called PBN, which enables the progressive extraction of high-frequency information from low-resolution (LR) to reconstruct high-resolution (HR) face images. To ensure the accuracy of obtaining high-frequency signals, we introduce a constraint from HR to LR, which constructs supervised learning by progressively downsampling the reconstructed image to an LR space. Specifically, we propose a triple-attention fusion block to focus on different local features and prevent the secondary loss of facial structural information by removing the pooling layers. Experiments demonstrate the superior performance of the proposed method quantitatively and qualitatively on three widely used public face datasets (i.e., CelebA, FFHQ, and LFW) compared to existing state-of-the-art methods.