Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
This work addresses noise removal in hyperspectral images, which is an incremental improvement for image processing applications.
The paper tackled mixed Gaussian and sparse noise removal in hyperspectral images by proposing a constrained low-tubal-rank tensor recovery model, achieving effective denoising with an efficient iterative algorithm based on bilateral random projections.
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.