Noise Robust Face Image Super-Resolution Through Smooth Sparse Representation

Keywords

Face image; fused least absolute shrinkage and selection operator (LASSO); smoothness constraint; sparse representation (SR); super-resolution

Abstract

Face image super-resolution has attracted much attention in recent years. Many algorithms have been proposed. Among them, sparse representation (SR)-based face image super-resolution approaches are able to achieve competitive performance. However, these SR-based approaches only perform well under the condition that the input is noiseless or has small noise. When the input is corrupted by large noise, the reconstruction weights (or coefficients) of the input low-resolution (LR) patches using SR-based approaches will be seriously unstable, thus leading to poor reconstruction results. To this end, in this paper, we propose a novel SR-based face image super-resolution approach that incorporates smooth priors to enforce similar training patches having similar sparse coding coefficients. Specifically, we introduce the fused least absolute shrinkage and selection operator-based smooth constraint and locality-based smooth constraint to the least squares representation-based patch representation in order to obtain stable reconstruction weights, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FEI face database and CMU+MIT face database. Visual and quantitative comparisons show that the proposed face image super-resolution method yields superior reconstruction results when the input LR face image is contaminated by strong noise.

Publication Date

11-1-2017

Publication Title

IEEE Transactions on Cybernetics

Volume

47

Issue

11

Number of Pages

3991-4002

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TCYB.2016.2594184

Socpus ID

85035780360 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85035780360

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