Hyperspectral image superresolution: An edge-preserving convex formulation

Autor:	Simões, Miguel, Bioucas-Dias, José, Almeida, Luis B., Chanussot, Jocelyn
Rok vydání:	2014
Předmět:	Computer Science - Computer Vision and Pattern Recognition Physics - Data Analysis Statistics and Probability Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. These complementary characteristics have stimulated active research in the inference of images with high spatial and spectral resolutions from HSI-MSI pairs. In this paper, we formulate this data fusion problem as the minimization of a convex objective function containing two data-fitting terms and an edge-preserving regularizer. The data-fitting terms are quadratic and account for blur, different spatial resolutions, and additive noise; the regularizer, a form of vector Total Variation, promotes aligned discontinuities across the reconstructed hyperspectral bands. The optimization described above is rather hard, owing to its non-diagonalizable linear operators, to the non-quadratic and non-smooth nature of the regularizer, and to the very large size of the image to be inferred. We tackle these difficulties by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA)---an instance of the Alternating Direction Method of Multipliers (ADMM)---to this optimization problem. By using a convenient variable splitting and by exploiting the fact that HSIs generally "live" in a low-dimensional subspace, we obtain an effective algorithm that yields state-of-the-art results, as illustrated by experiments. Comment: International Conference on Image Processing (ICIP), 2014 - accepted
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1403.8098 Zobrazit plný text záznamu View this record from Arxiv