Noise reduction of hyperspectral imagery based on nonlocal tensor factorization

Noise reduction for hyperspectral imagery (HSI) is an indispensable step before further processes such as object detection and classification. In this paper, we propose a noise reduction method for HSI based on non-local strategy and tensor factorization. Based on the observation that natural images are always locally self-repetitive, we divide the whole HSI into small sub-blocks and cluster similar blocks into groups. Since similar blocks share the same underlying structure, the redundancy can be utilized to remove noise of the blocks jointly. We stack the similar blocks to construct a fourth-order tensor from each group. Noise is reduced by finding the lower dimensional approximation of each of the fourth-order tensors via Tucker factorization. The experimental results indicate that the proposed method has a good quality of restoring the true signal from the noisy observation.