Hyperspectral Image Denoising Using Tensorial Weighted Schatten-$p$ Norm with Graph Laplacian Regularization

As an advanced spectral imaging technology, hyperspectral has critical applications in remote sensing. Unfortunately, hyperspectral images (HSIs) are frequently contaminated by diverse noise interference during capture. It is desirable to remove these mixed noises and recover clean HSIs accurately. Current approaches struggle to deliver great performance because they fail to effectively utilize the spectral correlations in hyperspectral data. This paper introduces an innovative hyperspectral image denoising algorithm based on the tensorial weighted Schatten-p norm and graph Laplacian regularization named TWSPGLR. Firstly, to improve the accuracy of low-rank tensor recovery, the tensorial weighted Schatten- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> norm is introduced to recover clean hyperspectral data. Secondly, we introduce a spectral constraint to enhance restoration accuracy by efficiently exploiting the spectral correlations of hyperspectral data. Finally, experimental results demonstrate the superiority of TWSPGLR compared with the state-of-the-art methods for HSI denoising.