… The denoising of 2D images through low-rank methods is a relevant topic in digital image …
involved in the low-rank denoising of 2D images. To improve the denoising results, we apply …

… To compare with HSI-DeNet, ie, state-of-the-art deep learning based method [29], we use the
test HSI5 … However, deep learning based methods require extra data for training and their …

… denoising scheme, namely deep low-rank prior (DLRP), which includes the following key
points: First, the low-rank … While the other is a low-rank minimization denoising problem and is …

… Remote sensing image noise reduction methods can be roughly categorized into the … deep
learning-based methods. First, the traditional remote sensing image noise reduction methods …

… Firstly, we decompose the data into a low-rank matrix and a sparse matrix in label subspace…
the low-rank matrix and sparse matrix to preserve the details, and then we use the low-rank …

… , we develop a nonlocal low-rank plus deep denoising prior model to simultaneously capture
… When the denoising algorithm is a deep learning based denoiser, it is usually termed as …

… 1.2 Deep learning-based methods As opposed to sparse representation-based methods
using hand-crafted features for image modeling, deep learning-based methods can adaptively …

… denoising convolutional neural network (ADMM-CNN) by combining low-rank decomposition
with feed-forward denoising … DnCNN is a deep-learning-based method for noise removal, …

… model for image denoising, designated as the bilateral weighted sparse coding and low-rank
(… Besides the model-driven image denoising methods, deep learning-based methods have …

… in all deep learning-based approaches that limit their efficacy. It is observed that the N2N
method, an unsupervised deep learning-based, exhibits effective noise reduction, albeit less …