Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction

Hyperspectral images (HSIs) can be very noisy, and the amount of noise may differ from band to band. While some spectral bands may be dominated by low signal-independent noise levels, others have mixed noise levels, which may include high levels of Gaussian, Poisson, and Spike noises. When a denoising algorithm is globally applied to the whole data set, it usually affects the low-noise bands adversely. Therefore, it is better to use different criteria for denoising different bands. In this paper, we propose a new denoising strategy to do so. The method is based on a 2-D nonsubsampled shearlet transform, applied to each spectral band of the HSI. We propose an effective method to distinguish between bands with low levels of Gaussian noise (LGN bands) and bands with mixed noise (MN bands) based on spectral correlation. LGN bands are denoised using a thresholding technique on the shearlet coefficients. On the MN bands, a local noise reduction method is applied, in which the detail shearlet coefficients of adjacent LGN bands are employed. This targeted approach is prone to reduce spectral distortions during denoising compared with global denoising methods. This advantage is shown in experiments where the proposed method is compared with state-of-the-art denoising methods on synthetic and real hyperspectral data sets. To assess the effect of denoising, classification and spectral unmixing tasks are applied to the denoised data. Obtained results show the superiority of the proposed approach.