Nonlocal regularized hyperspectral image denoising algorithm based on tensor ring decomposition

Hyperspectral images have rich spectral features and widely application. There are two problems in removing noise from hyperspectral images, on the one hand, in the acquisition and transmission process, hyperspectral images (HSIs) is unavoidably corrupted by several types of noise which makes people unable to obtain information quickly and accurately. On the other hand, most of the traditional denoising algorithm are carried out on Tucker or CANDECOMP/PARAFAC (CP), which converts high-dimensional signals into low-dimensional signals, thus changing the inherent structure of the signals. Inherent structure, it is difficult to optimally estimate the rank of tensor, and the parameters involved make the amount of calculation large.To solve the above problems, this paper proposes a non-local regularized hyperspectral image denoising (TRTD-NRM) based on tensor ring decomposition. The algorithm uses the characteristics of Tensor ring decomposition algorithm directly processes high-dimensional signals to study global spectral correlation (GCS) and spatial non-local self-similarity (NSS), which can be easily calculated and retain the inherent properties of hyperspectral images. Design an alternating direction multiplier method to solve the model. The image after removing the noise is very clear. Numerical experiments show that the noise-removed image obtained by this method is very clear. Compared to existing algorithms, the proposed algorithm is highly competitive both in subjective and objective effects.