Irregular Tensor Representation for Superpixel- Guided Hyperspectral Image Denoising

Recently, due to the ability to exploit perceptual information, superpixel-based methods have received attention for hyperspectral image (HSI) denoising. However, existing superpixel-based denoising methods unfold the irregular 3-D superpixels into the matrices along the spectral mode, which inevitably destroys the intrinsic structure of the irregular 3-D superpixels. To tackle the irregular 3-D superpixels, we introduce the irregular tensor representation for superpixel-guided HSI denoising. More concretely, by introducing the weighted tensor, we suggest a tensor representation of each irregular 3-D superpixel, which can preserve the intrinsic structure of the irregular 3-D superpixel. Equipped with the irregular tensor representation, we establish a superpixel-guided tensor optimization model for HSI denoising, which simultaneously exploits the perceptual information and low-rank structure within 3-D superpixels. To solve the resulting tensor optimization problem, we develop an inexact augmented Lagrange multiplier (IALM) algorithm. Experimental results show that the proposed method outperforms other state-of-the-art HSI denoising methods, particularly matrix-based methods, on both simulated and real data.