With the wealth of available spatial and spectral information

hyperspectral remote sensing images have been used for many remote sensing applications and have attracted considerable attention.However

most hyperspectral remote sensing images suffer from degradation because of the distortion of atmospheric transmission

the limitation of electronic devices

and the influence of poor illumination.The degraded data can lead to seriously inaccurate results in the subsequent applications.Thus

on the basis of the low-rank representation and the mixture total variation regularization in this study

a new model is proposed to restore hyperspectral remote sensing images. First

two types of low rank-based priori information in the hyperspectral remote sensing images

i.e.

the low rank-based priori information in the spectral domain and the low rank-based priori information in the spatial domain

are explored.Then

on the basis of the low rank-based priori information in the spectral domain

a low-rank representation model is proposed to suppress sparse noises

such as impulse

stripe

and dead line noises.Subsequently

on the basis of the low rank-based priori information in the spatial domain

the mixture total variation regularization method is proposed to suppress density noises

such as Gaussian and Poisson noises.The mixture total variation is the linear combination of the anisotropic and isotropic total variations and is more approximate to the zero norm than the anisotropic and isotropic total variations.The restoration results of the mixture total variation regularization method are better than those of the traditional total variation-based methods.Finally

the low-rank representation and mixture total variation regularization models are integrated.As such

the new restoration model based on the low-rank representation and mixture total variation regularization possesses the advantages of the two aforementioned models. This study tests the performance of the proposed method with a set of challenging hyperspectral remote sensing images.The simulated noises are Gaussian

Poisson

salt-and-pepper

and dead line noises.The intensities of the mixture noise in each band are different

thereby enabling the simulation of the real situations as approximately as possible.The restoration results of the proposed method are compared with those of several related methods.After restoring the image

the peak signal-to-noise ratio and structural similarity indices are adopted to provide quantitative assessments of the results of the experiments.All the experiments prove that the proposed method achieves better visual quality and quantitative indices than those of several existing related methods. The proposed model relies on the low rank-based priori knowledge in the spectral and spatial domains

efficiently suppresses the sparse and density noises in the degraded hyperspectral remote sensing images

and finally restores better hyperspectral remote sensing images than those of existing methods.The proposed model can be extended to other fields of remote sensing applications.