Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches.

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constraint on these 4-D tensors, which can effectively learn the correlations among the spatial, spectral, and nonlocal modes because of the well-balanced matricization scheme of TT rank. We formulate the super-resolution problem as TT rank regularized optimization problem, which is solved via the scheme of alternating direction method of multipliers. Experiments on HSI data sets indicate the effectiveness of the LTTR-based method.

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and time-consuming. This paper presents a deep HSI sharpening method (named DHSIS) for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning. The DHSIS method incorporates the learned deep priors into the LR-HSI and HR-MSI fusion framework. Specifically, we first initialize the HR-HSI from the fusion framework via solving a Sylvester equation. Then, we map the initialized HR-HSI to the reference HR-HSI via deep residual learning to learn the image priors. Finally, the learned image priors are returned to the fusion framework to reconstruct the final HR-HSI. Experimental results demonstrate the superiority of the DHSIS approach over existing state-of-the-art HSI sharpening approaches in terms of reconstruction accuracy and running time.

Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular scheme to enhance the spatial resolution of HSI. We propose a novel subspace-based low tensor multi-rank regularization method for the fusion, which fully exploits the spectral correlations and non-local similarities in the HR-HSI. To make use of high spectral correlations, the HR-HSI is approximated by spectral subspace and coefficients. We first learn the spectral subspace from the LR-HSI via singular value decomposition, and then estimate the coefficients via the low tensor multi-rank prior. More specifically, based on the learned cluster structure in the HR-MSI, the patches in coefficients are grouped. We collect the coefficients in the same cluster into a three-dimensional tensor and impose the low tensor multi-rank prior on these collected tensors, which fully model the non-local self-similarities in the HR-HSI. The coefficients optimization is solved by the alternating direction method of multipliers. Experiments on two public HSI datasets demonstrate the advantages of tour method.

Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a common scheme to obtain high-resolution HSI (HR-HSI). This article presents a novel HSI and MSI fusion method (called as CNN-Fus), which is based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising. Our method only needs to train the CNN on the more accessible gray images and can be directly used for any HSI and MSI data sets without retraining. First, to exploit the high correlations among the spectral bands, we approximate the desired HR-HSI with the low-dimensional subspace multiplied by the coefficients, which can not only speed up the algorithm but also lead to more accurate recovery. Since the spectral information mainly exists in the LR-HSI, we learn the subspace from it via singular value decomposition. Due to the powerful learning performance and high speed of CNN, we use the well-trained CNN for gray image denoising to regularize the estimation of coefficients. Specifically, we plug the CNN denoiser into the alternating direction method of multipliers (ADMM) algorithm to estimate the coefficients. Experiments demonstrate that our method has superior performance over the state-of-the-art fusion methods.

Hyperspectral image (HSI) super-resolution, which fuses a low-resolution (LR) HSI with a high-resolution (HR) multispectral image (MSI), has recently attracted much attention. Most of the current HSI super-resolution approaches are based on matrix factorization, which unfolds the three-dimensional HSI as a matrix before processing. In general, the matrix data representation obtained after the matrix unfolding operation makes it hard to fully exploit the inherent HSI spatial-spectral structures. In this paper, a novel HSI super-resolution method based on non-local sparse tensor factorization (called as the NLSTF) is proposed. The sparse tensor factorization can directly decompose each cube of the HSI as a sparse core tensor and dictionaries of three modes, which reformulates the HSI super-resolution problem as the estimation of sparse core tensor and dictionaries for each cube. To further exploit the non-local spatial self-similarities of the HSI, similar cubes are grouped together, and they are assumed to share the same dictionaries. The dictionaries are learned from the LR-HSI and HR-MSI for each group, and corresponding sparse core tensors are estimated by spare coding on the learned dictionaries for each cube. Experimental results demonstrate the superiority of the proposed NLSTF approach over several state-of-the-art HSI super-resolution approaches.