Hyperspectral Denoising Via Global Tensor Ring Decomposition and Local Unsupervised Deep Image Prior

Recently, unsupervised deep learning-based methods have shown an empirical success in hyperspectral images (HSIs) denoising, profiting from the strong representation ability of convolutional neural networks. However, these methods only can describe the local structure of the spatial dimension, which is restricted to very limited local receptive fields. To overcome this difficulty, a novel HSIs denoising model based on the deep image prior (DIP) framework is proposed by adding a tensor ring (TR) decomposition, which can enlarge the receptive field of the spatial dimension and capture global spectral correlation simultaneously. Unlike the previous DIP framework that directly minimizes the objective function, we develop an algorithm based on proximal alternating minimization to decouple the model into the classic DIP framework and TR cores least-squares problems, which are easy to solve. Experimental results verify that the proposed DIP- TR compares favorably with compared methods in terms of quality metrics and visual performance.