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Found 10 papers, 2 papers with code
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
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.
An Extension of Averaged-Operator-Based Algorithms
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.
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
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.
Restoring STM images via Sparse Coding: noise and artifact removal
In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images.
A Framework for Fast Image Deconvolution with Incomplete Observations
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.
Image Classification with Rejection using Contextual Information
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information.
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution.
Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
This paper presents a variational based approach to fusing hyperspectral and multispectral images.
Hyperspectral image superresolution: An edge-preserving convex formulation
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.
