Face hallucination through dual associative learning

In this paper, we propose a novel patch-based face hallucination framework, which employs a dual model to hallucinate different components associated with one facial image. Our model is based on a statistical learning approach: Associative Learning. It suffices to learn the dependencies between low-resolution image patches and their high-resolution ones with a new concept Hidden Parameter Space as a bridge to connect those patches with different resolutions. To compensate higher frequency information of images, we present a dual associative learning algorithm for orderly inferring main components and high frequency components of faces. The patches can be finally integrated to form a whole high-resolution image. Experiments demonstrate that our approach does render high quality superresolution faces.

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Wei Liu, Dahua Lin, Xiaoou Tang
• Keywords: Concept Hidden Parameter, Dual Associative Learning, Face Hallucination Framework, ICIP 2005, Image Processing, Low-resolution Image Patches, Statistical Learning Approach