Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) conventional image into an HR HSI has become a prevalent HSIs super-resolution scheme. However, in most previous works, little attention has been paid on exploiting the underlying manifold structure in the spatial domain of the latent HR HSI. In this paper, we advance a provable prior knowledge that the clustering manifold structure of the latent HSI can be well preserved in the spatial domain of the input conventional image. Inspired by this, we first conduct clustering in the spatial domain of the input conventional image and adopt the intra-cluster self-expressiveness model to implicitly depict the clustering manifold structure, which enables learning the complicated manifold structure via solving a constrained ridge regression model without knowing the exact form of the manifold. Then, we incorporate the learned structure into a variational super-resolution framework to regularize the latent HSI. The resulted framework can be effectively optimized by a standard alternating direction method of multipliers. Since the learned structure can well depict the underlying spatial manifold of the latent HSI, the proposed method shows the state-of-the-art super-resolution performance on two benchmark data sets.

The production of a high spatial resolution (HR) hyperspectral image (HSI) through the fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has underpinned much of the recent progress in HSI super-resolution. The premise of these signs of progress is that both the degeneration from the HR HSI to LR HSI in the spatial domain and the degeneration from the HR HSI to HR MSI in the spectral domain are assumed to be known in advance. However, such a premise is difficult to achieve in practice. To address this problem, we propose to incorporate degeneration estimation into HSI super-resolution and present an unsupervised deep framework for ``blind'' HSIs super-resolution where the degenerations in both domains are unknown. In this framework, we model the latent HR HSI and the unknown degenerations with deep network structures to regularize them instead of using handcrafted (or shallow) priors. Specifically, we generate the latent HR HSI with an image-specific generator network and structure the degenerations in spatial and spectral domains through a convolution layer and a fully connected layer, respectively. By doing this, the proposed framework can be formulated as an end-to-end deep network learning problem, which is purely supervised by those two input images (i.e., LR HSI and HR MSI) and can be effectively solved by the backpropagation algorithm. Experiments on both natural scene and remote sensing HSI data sets show the superior performance of the proposed method in coping with unknown degeneration either in the spatial domain, spectral domain, or even both of them.

Unsupervised fusion-based hyperspectral imagery (HSI) super-resolution (SR) is an essential task of HSI processing, which aims to reconstruct a high-resolution (HR) HSI using only an observed low-resolution HSI and a conventional HR image. Although a large number of unsupervised HSI SR methods have been proposed, the heuristic handcrafted image priors adopted by the majority of these methods restrict their capacity to capture specific characteristics of the HSI, as well as their ability to generalize to noisy observation images. In this study, we investigate a fusion-based HSI SR framework with the deep image prior, in which the deep neural network (rather than a heuristic handcrafted image prior) is exploited to capture plenty of image statistics. Within this framework, we further propose an unsupervised recurrence-based HSI SR method using pixel-aware refinement, which utilizes the intermediate reconstruction results to self-supervise unsupervised learning. Due to containing the information of the image-specific characteristic, the proposed method achieves better performance, in terms of both accuracy and robustness to noise, compared with the existing methods. Extensive experiments on four HSI data sets demonstrate the effectiveness of the proposed method.

Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate class-level semantic descriptions and be assigned to the nearest class. In this process, the embedding space underpins the success of such matching and is crucial for ZSL. In this paper, we conduct an in-depth study on the construction of embedding space for ZSL and posit that an ideal embedding space should satisfy two criteria: intra-class compactness and interclass separability. While the former encourages the embeddings of visual samples of one class to distribute tightly close to the semantic description embedding of this class, the latter requires embeddings from different classes to be well separated from each other. Towards this goal, we present a simple but effective twobranch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier. Furthermore, we extend our method to a transductive setting to better handle the model bias problem in ZSL (i.e., samples from unseen classes tend to be categorized into seen classes) with minimal extra supervision. Specifically, we propose a pseudo labeling strategy to progressively incorporate the testing samples into the training process and thus balance the model between seen and unseen classes. Experimental results on five standard ZSL datasets show the superior performance of the proposed method and its transductive extension.