Paper Info
Title
Robust Face Image Super-Resolution via Joint Learning of Subdivided Contextual Model.
Abstract
In this paper, we focus on restoring high-resolution facial images under noisy low-resolution scenarios. This problem is a challenging problem as the most important structures and details of captured facial images are missing. To address this problem, we propose a novel local patch-based Face Super-Resolution (FSR) method via the joint learning of the contextual model. The contextual model is based on the topology consists of contextual sub-patches, which provide more useful structural information than the commonly used local contextual structures due to the finer patch size. In this way, the contextual models are able to recover the missing local structures in target patches. In order to further strengthen the structural compensation function of contextual topology, we introduce the recognition feature as additional regularity. Based on the contextual model, we formulate the super-resolved procedure as a contextual joint representation with respect to the target patch and its adjacent patches. The high-resolution image is obtained by weighting contextual estimations. Both quantitative and qualitative validation shows that the proposed method performs favorably against state-of-the-art algorithms.
Year
DOI
Venue
2019
10.1109/TIP.2019.2920510
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
Field
DocType
Face,Context modeling,Topology,Image resolution,Image restoration,Image recognition,Surveillance
Computer vision,Weighting,Pattern recognition,Contextual design,Context model,Artificial intelligence,Image restoration,Image resolution,Superresolution,Mathematics
Journal
Volume
Issue
ISSN
28
12
1941-0042
Citations 
PageRank 
References 
2
0.38
23
Authors
3
Name
Order
Citations
PageRank
Liang Chen1629.36
Jin-shan Pan256730.84
Qing Li33222433.87