Affordable Access

Publisher Website

Parts-based face super-resolution via non-negative matrix factorization

Authors
Journal
Computers & Electrical Engineering
0045-7906
Publisher
Elsevier
Identifiers
DOI: 10.1016/j.compeleceng.2014.04.016
Disciplines
  • Computer Science

Abstract

Abstract Face super-resolution refers to inferring the high-resolution face image from its low-resolution one. In this paper, we propose a parts-based face hallucination framework which consists of global face reconstruction and residue compensation. In the first phase, correlation-constrained non-negative matrix factorization (CCNMF) algorithm combines non-negative matrix factorization and canonical correlation analysis to hallucinate the global high-resolution face. In the second phase, the High-dimensional Coupled NMF (HCNMF) algorithm is used to compensate the error residue in hallucinated images. The proposed CCNMF algorithm can generate global face more similar to the ground truth face by learning a parts-based local representation of facial images; while the HCNMF can learn the relation between high-resolution residue and low-resolution residue to better preserve high frequency details. The experimental results validate the effectiveness of our method.

There are no comments yet on this publication. Be the first to share your thoughts.