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To make the best use of the underlying structure of faces, the collectiveinformation through face datasets and the intermediate estimates during theupsampling process, here we introduce a fully convolutional multi-stage neuralnetwork for 4$\times$ super-resolution for face images. We implicitly imposefacial component-wise attention maps using a segmentation network to allow ournetwork to focus on face-inherent patterns. Each stage of our network iscomposed of a stem layer, a residual backbone, and spatial upsampling layers.We recurrently apply stages to reconstruct an intermediate image, and thenreuse its space-to-depth converted versions to bootstrap and enhance imagequality progressively. Our experiments show that our face super-resolutionmethod achieves quantitatively superior and perceptually pleasing results incomparison to state of the art.

Component Attention Guided Face Super-Resolution Network: CAGFace