In this article, we have reviewed the reasons why we (collectively) want to love or leave the
venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal …

With the increasing demand for image-based applications, the efficient and reliable
evaluation of image quality has increased in importance. Measuring the image quality is of …

Nonlinear models have recently shown interesting properties for spectral unmixing. This
paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for …

As a powerful and promising statistical signal modeling technique, sparse representation
has been widely used in various image processing and analysis fields. For hyperspectral …

This paper studies a fully Bayesian algorithm for endmember extraction and abundance
estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed …

This letter presents a novel image quality index which extends the Universal Image Quality
Index for monochrome images to multispectral and hyperspectral images through …

Transform-based lossy compression has a huge potential for hyperspectral data reduction.
Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension …

Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It
amounts at estimating the spectral signatures of the pure spectral constituents in the scene …

In this paper, a novel spectral–spatial adaptive sparse representation (SSASR) method is
proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at …

The presence of an imbalance caused by an insufficient level of performance of modern and
promising infocommunication technologies with respect to the information intensity of bit …