Hyperspectral image mixed noise removal via tensor robust principal component analysis with tensor-ring decomposition

Due to the instability of sensors and other factors, hyperspectral images (HSIs) are inevitably polluted by various types of mixed noise. To explore a better denoising method based on the existing research, combining the denoising advantages of tensor-ring (TR) decomposition and tensor robust principal component analysis (TRPCA), a mixed-noise removal method for HSIs is proposed in this paper. First, TRPCA maintains the tensor structure of the image itself, accurately recovers the low-rank part and the sparse part from their sum and separates the sparse noise in the form of sparse tensors. Then, TR decomposition is introduced to denoise the low-rank tensors. To verify the effectiveness and superiority of this method, experiments are carried out on two simulated data sets and two real data sets. Compared with the traditional denoising methods and several existing improved denoising methods from both visual and quantitative aspects, the proposed TRPCA-TR method provides better denoising results.

Automated summary

This paper proposes a mixed-noise removal method for hyperspectral images (HSIs) by combining the denoising advantages of tensor-ring (TR) decomposition and tensor robust principal component analysis (TRPCA). The proposed method accurately recovers the low-rank and sparse parts of the image and separates the sparse noise in the form of sparse tensors. Experimental results demonstrate that the proposed TRPCA-TR method outperforms traditional denoising methods and existing improved denoising methods in terms of visual and quantitative evaluations.