Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

TL;DR

This paper introduces a new low-tubal-rank tensor recovery method with bilateral random projections for effectively removing mixed noise from hyperspectral images, improving denoising accuracy and efficiency.

Contribution

It proposes a novel constrained low-tubal-rank tensor model and an efficient bilateral random projection algorithm for hyperspectral image denoising.

Findings

Effective removal of mixed Gaussian and sparse noise

High accuracy in low-tubal-rank tensor approximation

Demonstrated efficiency and effectiveness on hyperspectral images

Abstract

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.