Face Hallucination From New Perspective of Non-Linear Learning Compressed Sensing

The past decade has witnessed a prosperity of sparsity-inspired face hallucination methods that use sparse prior and instances to generate High-Resolution (HR) faces. However, they need numerous Low-Resolution (LR) and HR instance pairs and adopt approximate sparse coding, which will bring bias to t...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.9434-9440
Main Authors: Yang, Shuyuan, Hao, Xiaoyang, Liu, Zhi, Yang, Chen, Wang, Min
Format: Article
Language:English
Subjects:
Compressed sensing
deep autoencoder
Dictionaries
Encoding
Face
Face hallucination
Hallucinations
Image coding
Kernel
Kernel functions
Learning
non-linear learning compressed sensing
nonlinear sparse coding
Recovery
Sparsity
Training
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Summary:The past decade has witnessed a prosperity of sparsity-inspired face hallucination methods that use sparse prior and instances to generate High-Resolution (HR) faces. However, they need numerous Low-Resolution (LR) and HR instance pairs and adopt approximate sparse coding, which will bring bias to the recovery and suffer from high computational burden. In this paper we advance a Single Face Image Hallucination (SFIH) method from a new perspective of Non-linear Learning Compressive Sensing (NLCS), which can recover HR faces from a surprisingly small number of HR faces. The nonlinear sparse coding of facial images is explored, and a Deep AutoEncoder (DAE) network is constructed for learning a kernel function from a single HR instance set. SFIH is then reduced to an analytic compressive recovery problem by reformulating linear sparse coding as a nonlinear DAE model. By exploring the nonlinear sparsity in the feature space, NLCS can accurately and rapidly recover HR facial images with large magnification factor and exhibit robustness to LR-HR instance pairs mapping. Some experiments are taken on realizing 3X, 6X, 9X amplification of face images, and the results prove its efficiency and superiority to its counterparts.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2963360