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We propose a regularization function for hyperspectral image restoration based on a newly-designed structure tensor. We adopt a convex optimization approach with the use of the nuclear norm of a matrix, termed as spatio-spectral structure tensor. It consists of the gradient components of a hyperspectral image cube w.r.t. the spatio-spectral domain. The proposed approach allows to penalize variations...
Hyperspectral sparse unmixing is a task to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. The abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. It implies the low rankness...
We propose a method for local spectral component decomposition based on the line feature of local distribution. Our aim is to reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. We first calculate a linear feature over the spectral components of an $M$ -channel image, which we call the spectral line, and then, using the line, we decompose...
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