The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.
Many of the algorithms used to solve minimization problems with sparsity-inducing regularizers are generic in the sense that they do not take into account the sparsity of the solution in any particular way.
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images.
In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information.
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.
Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution.
Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions.