Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising

Hyperspectral imaging (HSI) has been used in a wide range of applications recent years. But the process image acquisition, hyperspectral images are subject to various types noise interference. Noise reduction algorithms can be enhance quality and make it easier detect analyze features interest. To realize better recovery, we propose weighted group sparsity-regularized low-rank tensor ring decomposition (LRTRDGS) method for recovery. Tensor utilized by this approach investigate self-similarity global spectral correlation. Furthermore, sparsity regularization employed depict structure along dimension spatial difference image. Moreover, solve proposed model using symmetric alternating direction multiplier with addition proximity term. The experimental data verify effectiveness our method.