A joint learning based face hallucination approach for low quality face image

This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses coupled feature spaces under surveillance scenarios. For surveillance face images, traditional neighbor embedding SR approaches could not offer counterintuitive results because consistency between high resolution images and low resolution images is destroyed by serious noise which caused by environmental impact factors and large distance between the camera and objects. In order to reinforce the consistency, we extend the learning space from single to a coupled feature space that combine image intensity feature and contour model. The contour model describes facial contour information as images generated from original low resolution ones. Simulation experiments show that this proposed approach could provide competitive results in simulation experiments in subjective and objective quality. Even in surveillance scenario the proposed method outperforms the traditional methods.