390 research outputs found
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Face image super-resolution using 2D CCA
In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V
Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning
Extensive attention has been widely paid to enhance the spatial resolution of
hyperspectral (HS) images with the aid of multispectral (MS) images in remote
sensing. However, the ability in the fusion of HS and MS images remains to be
improved, particularly in large-scale scenes, due to the limited acquisition of
HS images. Alternatively, we super-resolve MS images in the spectral domain by
the means of partially overlapped HS images, yielding a novel and promising
topic: spectral superresolution (SSR) of MS imagery. This is challenging and
less investigated task due to its high ill-posedness in inverse imaging. To
this end, we develop a simple but effective method, called joint sparse and
low-rank learning (J-SLoL), to spectrally enhance MS images by jointly learning
low-rank HS-MS dictionary pairs from overlapped regions. J-SLoL infers and
recovers the unknown hyperspectral signals over a larger coverage by sparse
coding on the learned dictionary pair. Furthermore, we validate the SSR
performance on three HS-MS datasets (two for classification and one for
unmixing) in terms of reconstruction, classification, and unmixing by comparing
with several existing state-of-the-art baselines, showing the effectiveness and
superiority of the proposed J-SLoL algorithm. Furthermore, the codes and
datasets will be available at:
https://github.com/danfenghong/IEEE\_TGRS\_J-SLoL, contributing to the RS
community
Hyperspectral super-resolution of locally low rank images from complementary multisource data
International audienceRemote sensing hyperspectral images (HSI) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images (MSI) in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods decrease mainly because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSI are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution via local dictionary learning using endmember induction algorithms (HSR-LDL-EIA). We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data
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