Hyperspectral images (HSIs) are prone to be corrupted by various types of noise during the process of imaging and transmission, which seriously affect the subsequent HSI processing tasds. In this paper, we proposed a novel low-rank based model for HSIs denoising. On one hand, motivated by the superiority of nonconvex approximation to matrix rank, we construct a new nonconvex function which is tighter than some of existing rank approximated functions. On the other hand, we describe Gaussian noise and sparse noise simultaneously by introducing correntropy. In comparison with traditional model, we constrain noise in one regularization instead of separately constraining Gaussian noise and sparse noise which indicates that the number of regularization parameters have been reduced. To optimize the proposed model, some convex analysis tools are utilized in this paper. Additionally, we provide theoretical analysis on the convergence of the developed algorithm. Through experiments conducted on both simulated and real HSIs, we verify the superiority of our model in enhancing the performance of mixture noise removal in HSIs.

HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm.

The noise in hyperspectral images (HSIs) is mixed with signal-independent (SI) thermal noise and signal-dependent (SD) photon noise. In this letter, a mixed noise estimation algorithm is proposed to estimate the noise level of SI and SD noise, respectively. The proposed algorithm removes the spectral correlation of the HSI by multiple linear regression (MLR) on several neighboring bands. According to the relationship between the prediction coefficients by MLR and the local statistics, an overdetermined equation system with respect to the variances of the SI and SD noise is established. Then the estimated noise variances are solved by the least-squares (LS) method. To ensure the accuracy of local statistics, regional clustering-based spatial preprocessing (RCSP) for HSI superpixel segmentation is employed to detect the homogeneous region. Experimental results on the simulated data confirm considerable improvements, and the real-life data experiment is implemented to validate the performance of the proposed algorithm.