Structured Face Hallucination

The goal of face hallucination is to generate high-resolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and smooth regions. The image structure is maintained via matching gradients in the reconstructed high-resolution output. For facial components, we align input images to generate accurate exemplars and transfer the high-frequency details for preserving structural consistency. For contours, we learn statistical priors to generate salient structures in the high-resolution images. A patch matching method is utilized on the smooth regions where the image gradients are preserved. Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability.

1. Introduction

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) images from low-resolution (LR) inputs, which finds numerous vision applications. Since a LR image can be modeled from a HR image by a linear convolution process with downsampling, the hallucination problem can be viewed as an inverse task to reconstruct the high-frequency details. While recent work focuses on the generic super-resolution problem, considerable less attention is paid to face halluci-nation. In this paper, we propose a face hallucination algorithm that exploits domain-specific image structures to generate HR results with high fidelity.