Mixed noise removal in hyperspectral images using low-rank tensor decomposition with spatial-spectral weighted sparsity regularization
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Hyperspectral image restoration
low-rank tensor decomposition
group sparsity
mixed noise
Tóm tắt
This paper addresses the problem of hyperspectral image (HSI) restoration under mixed noise, specifically Gaussian and impulse noise, by proposing a novel method based on low-rank tensor decomposition. While existing low-rank approaches often adopt fixed regularization schemes, they may fail to capture spatial-spectral correlations in HSIs. To overcome this limitation, we introduce a spatial-spectral adaptive model that integrates regularizers with pixel-wise weighting, enabling local structural preservation and improved denoising performance. An efficient optimization algorithm based on the Augmented Lagrangian Method (ALM) is developed, with closed-form solutions derived for subproblems to ensure both accuracy and convergence. Experimental results on both simulated and real HSI datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches, which are based on low-rank tensor decomposition. Our method achieves higher PSNR, SSIM, and FSIM scores, and provides visually cleaner results with better preservation of image details.
Tài liệu tham khảo
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Sep 30, 2025
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Cách trích dẫn
Tran, H. T. M., T. T. Huynh, T. V. Vu, và T. D. K. Phan. “Mixed Noise Removal in Hyperspectral Images Using Low-Rank Tensor Decomposition With Spatial-Spectral Weighted Sparsity Regularization”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 23, số p.h 9C, Tháng Chín 2025, tr 74-80, doi:10.31130/ud-jst.2025.23(9C).538E.
