Abstract
To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain spectral residual total variation (SSRTV). Considering that there is much residual texture information in spectral variation image, SSRTV first calculates the difference between the pixel values of adjacent bands and then calculates a 2DTV for the residual image. Experimental results demonstrated that the SSRTV regularization term is powerful at changing the structures of noises in an original HSI, thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. The global low-rankness and spatial–spectral correlation of HSI is exploited by low-rank Tucker decomposition (LRTD). Moreover, it was demonstrated that the l2,1 norm is more effective to deal with sparse noise, especially the sample-specific noise such as stripes or deadlines. The augmented Lagrange multiplier (ALM) algorithm was adopted to solve the proposed model. Finally, experimental results with simulated and real data illustrated the validity of the proposed method. The proposed method outperformed state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods in terms of quantitative metrics and visual inspection.
Highlights
We proposed a new low-rank tensor decomposition-based spatial domain spectral residual total variation-regularized technique for mixed noise removal of hyperspectral images (HSIs)
Different from the existing total variation (TV) regularization methods, both the direct spatial and spatial-spectral piecewise smoothness of an HSI were evaluated by spatial domain spectral residual total variation (SSRTV), leading to an effective way to remove Gaussian noise for HSI
HSI among all bands was described via low-rank tensor decomposition, which can aid in isolating the sparse noise from the clean HSI
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. To utilize the global low-rank property and spatial–spectral correlation of HSI, methods based on the low-rank tensor decomposition (LRTD) have been proposed for HSI restoration [12–14]. In addition to the low rank property of HSI, it has a piecewise smooth structure as a natural image in the spatial domain, which can be exploited by total variation (TV). In the design of SSTV and ASSTV, spatial correlation is interpreted as spectral piecewise smoothness and is evaluated by the l1 norm of local differences along the spectral direction, resulting in computationally efficient optimization. We designed a Spatial-Spectral Residual Total Variation (SSRTV) to better capture both the direct spatial and spatial-spectral piecewise smoothness of HSI This can overcome disadvantages of previous TV methods, that is, the low-rank regularization fails to remove the structured sparse noise.
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