Iterative optimal solutions of linear matrix equations for hyperspectral and multispectral image fusing
نویسندگان
چکیده
Abstract For a linear matrix function f in $$X \in {\mathbb {R}}^{m\times n}$$ X ∈ R m × n we consider inhomogeneous equations $$f(X) = E$$ f ( ) = E for $$E \ne 0$$ ≠ 0 that have or do not solutions. such systems compute optimal norm constrained solutions iteratively using the Conjugate Gradient and Lanczos’ methods combination with More–Sorensen optimizer. We build codes ten equations, of Sylvester, Lyapunov, Stein structured types their T-versions, differ only two five times repeated equation specific code lines. Numerical experiments are performed illustrate universality efficiency our method dense small data matrices, as well sparse certain input matrices. Specifically show how to adapt universal inputs encountered when fusing image sets via Sylvester algorithm obtain an higher resolution.
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ژورنال
عنوان ژورنال: Calcolo
سال: 2023
ISSN: ['0008-0624', '1126-5434']
DOI: https://doi.org/10.1007/s10092-023-00514-8