Abstract
Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.
Highlights
Hyperspectral images (HSIs) are recordings of reflectance of light of some real world scenes or objects including hundreds of spectral bands ranging from ultraviolet to infrared wavelength [1,2]
We provide some experimental analysis to show how we can choose them for practical use. (a) and (b) in Figure 7 describe the relationship between mean peak signal-to noise ratio (MPSNR) and the regularization parameters λ1 and λ2 when NLRTATV performed on DC Mall data under noise free setting, respectively, with the other parameters fixed at optimal values
We have proposed a novel method named NLRTATV for dealing with the problem of HSI super-resolution by using tensor structural modeling
Summary
Hyperspectral images (HSIs) are recordings of reflectance of light of some real world scenes or objects including hundreds of spectral bands ranging from ultraviolet to infrared wavelength [1,2]. Huang et al [18] presented a novel super-resolution approach of HSIs by joint low-rank and group-sparse modeling. Their approach can deal with the situation that the system blurring is unknown. In this paper, following the ideas of our preliminary work [22] and the work [23] for MRI super-resolution, we consider the HSI cube as the tensor with three modes, namely, width, height, and band, and exploit the underlying structures in both spatial and spectral domain by using direct tensor modeling techniques to achieve the spatial resolution enhancement.
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