Face hallucination using cascaded super-resolution and identity priors

TL;DR

This paper introduces a cascaded super-resolution CNN model with identity priors for high-quality face hallucination from very low-resolution images, outperforming existing methods.

Contribution

The novel cascaded super-resolution approach with multi-scale identity priors enhances face hallucination quality at high magnification factors.

Findings

Superior visual quality in upscaled facial images

Effective incorporation of identity constraints at multiple scales

Outperforms state-of-the-art face hallucination methods

Abstract

In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors. We approach the problem with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the low-resolution images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from competing super-resolution approaches that typically rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of $2\times$. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. Our model is able to upscale (very) low-resolution images captured in unconstrained conditions and produce visually convincing results. We rigorously evaluate the proposed model on a large datasets of facial images and report superior performance compared to the state-of-the-art.