Anisotropic Tensor Deconvolution of Hyperspectral Images

• URL: http://arxiv.org/abs/2601.11694v1
• Date: Fri, 16 Jan 2026 16:29:13 GMT
• Title: Anisotropic Tensor Deconvolution of Hyperspectral Images
• Authors: Xinjue Wang, Xiuheng Wang, Esa Ollila, Sergiy A. Vorobyov,
• Abstract summary: Hyperspectral image (HSI) deconvolution is a challenging ill-posed inverse problem, made difficult by the data's high dimensionality.<n>We propose a framework based on a low-parsimonious parameters of Canonical Poly Decomposition (CPD)<n>This approach recasts the problem from recovering a large-scale image with a latent to estimating the factors with $(P+Q+N)R$ variables.
• Score: 24.509592365283208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral image (HSI) deconvolution is a challenging ill-posed inverse problem, made difficult by the data's high dimensionality.We propose a parameter-parsimonious framework based on a low-rank Canonical Polyadic Decomposition (CPD) of the entire latent HSI $\mathbf{\mathcal{X}} \in \mathbb{R}^{P\times Q \times N}$.This approach recasts the problem from recovering a large-scale image with $PQN$ variables to estimating the CPD factors with $(P+Q+N)R$ variables.This model also enables a structure-aware, anisotropic Total Variation (TV) regularization applied only to the spatial factors, preserving the smooth spectral signatures.An efficient algorithm based on the Proximal Alternating Linearized Minimization (PALM) framework is developed to solve the resulting non-convex optimization problem.Experiments confirm the model's efficiency, showing a numerous parameter reduction of over two orders of magnitude and a compelling trade-off between model compactness and reconstruction accuracy.

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