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- W3119356265 abstract "In this paper, theoretical proofs and performance analysis of geometric algebra based singular value decomposition (GA-SVD) are provided for multispectral image. The multispectral image is represented as a multivector based on geometric algebra (GA). According to the GA multivector, its real representation is constructed. Then the SVD problem for multispectral image is solved by defining a relationship between the singular values given by the GA-SVD of the GA multivector and the real SVD of its real representation. The multispectral image denoising application is carried out by selecting several largest singular values and the corresponding unitary matrices. The denoising experiments with Gaussian noise and salt-and-pepper noise are implemented to validate its rationality and effectiveness. The higher PSNR value also shows its potential as an effective and homogeneous tool in multispectral image processing." @default.
- W3119356265 created "2021-01-18" @default.
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- W3119356265 date "2020-10-30" @default.
- W3119356265 modified "2023-09-26" @default.
- W3119356265 title "Theory and Performance Analysis of Geometric Algebra Based Singular Value Decomposition" @default.
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- W3119356265 doi "https://doi.org/10.1145/3436369.3436477" @default.
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