Abstract
Face hallucination aims to generate a high-resolution (HR) face image from an input low-resolution (LR) face image, which is a specific application field of image super resolution for face image. Due to the complex and sensitive structures of face image, obtaining a super-resolved face image is more difficult than generic image super resolution. Recently, deep learning based methods have been introduced in face hallucination. In this work, we develop a novel network architecture which integrates image super-resolution convolutional neural network with network style iterative back projection (IBP) method. Extensive experiments demonstrate that the proposed improved model can obtain better performance.
Published Version
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