Robust Low-Rank Analysis with Adaptive Weighted Tensor for Image Denoising

In order to obtain better denoising results, this paper proposes the Robust Low-Rank Analysis with Adaptive Weighted Tensor (AWTD) method for image denoising tasks. On one hand, it uses the latest adaptive weight tensor, which obtains the low-rank approximation of the tensor by adding adaptive weights to the unfolding matrix of the tensor. The adaptive weight tensor can effectively retain useful singular values and better preserve the low-rank properties of the unfolding matrix. On the other hand, the proposed algorithm considers the spatial information and spectral information at the same time: for the RGB images, it retains the structural information inside the image patch and the connection between different channels (the spatial information of the image); for the hyperspectral images, it also retains the spectral information of the hyperspectral images. The experimental results show that the proposed method is superior to other test methods.