Jiang Junjun, Ma Jiayi, Chen Chen, Jiang Xinwei, Wang Zheng
IEEE Trans Cybern. 2017 Nov;47(11):3991-4002. doi: 10.1109/TCYB.2016.2594184. Epub 2016 Aug 26.
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.
近年来，人脸图像超分辨率技术备受关注。人们提出了许多算法。其中，基于稀疏表示（SR）的人脸图像超分辨率方法能够取得具有竞争力的性能。然而，这些基于SR的方法仅在输入无噪声或噪声较小的情况下表现良好。当输入受到大量噪声干扰时，使用基于SR的方法对输入低分辨率（LR）补丁进行重建的权重（或系数）将严重不稳定，从而导致重建效果不佳。为此，在本文中，我们提出了一种新颖的基于SR的人脸图像超分辨率方法，该方法结合了平滑先验，以强制相似的训练补丁具有相似的稀疏编码系数。具体而言，我们将基于融合最小绝对收缩和选择算子的平滑约束以及基于局部性的平滑约束引入到基于最小二乘表示的补丁表示中，以获得稳定的重建权重，特别是当输入LR图像的噪声水平较高时。在基准FEI人脸数据库和CMU+MIT人脸数据库上进行了实验。视觉和定量比较表明，当输入LR人脸图像受到强噪声污染时，所提出的人脸图像超分辨率方法产生了优越的重建结果。
