Hyperspectral image restoration via superpixel segmentation of smooth band.

• A HSI restoration method using superpixel segmentation of smooth band is proposed, which exploits the spectral smooth and spatial-spectral similarity of the HSI. • Develop an efficient SSSB algorithm to solve the proposed method. • The method can better remove the mixed noise in the HSI, even for heavy noise. Hyperspectral images (HSIs) are inevitably degraded in the acquisition process by mixed noise including Gaussian noise, impulse noise, stripes, and so on. Recently, many low-rank regularization based HSI restoration methods have been proposed to powerfully remove the mixed noise. However, most of them use the square patch based denoising strategy, which destroyed the boundary information of the objects in the HSI. In this paper, we adopt superpixel segmentation to group the pixels of HSI with adjacent position, similar color, texture and luminance into a homogeneous region, whose shape is adaptive. Several homogeneous regions cover the full HSI. This is better than simply dividing the HSI into square patches. By taking advantage of both the low-rank property and the spectral smooth of the HSI, this approach can efficiently remove the mixed noise with few time. Several experiments verify the performance of the proposed approach for HSI restoration. [ABSTRACT FROM AUTHOR]