Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence
of observation conditions, which limits the availability of RS data. Therefore, it is of great …

The goal of dictionary learning algorithms is to learn a set of atoms called dictionary from a
set of training data such that each training data can be represented sparsely by the …

In this paper, a novel dictionary learning (DL) method is proposed to estimate sparse neural
activations from multi-subject fMRI data sets. By exploiting the label information such as the …

Recursive least squares (RLS) dictionary learning algorithm is one of the well-known
dictionary update approaches which continuously update the dictionary per arrival of new …

Reconstruction tasks in computer vision aim fundamentally to recover an undetermined
signal from a set of noisy measurements. Examples include super-resolution, image …

Convolutional sparse representations are receiving an increase attention as a better
alternative to the standard patch-based formulation for multiple image processing tasks …

The block recursive least square (BRLS) dictionary learning algorithm that dealing with
training data arranged in block is proposed in this paper. BRLS can be used to update …

Many state-of-the-art algorithms typically solve Tensor (multi-dimensional) problems using
linear algebra by vectorizing tensor signals. However, the size of the tensors increases in …

The main idea of image de-fencing refers to removing fence-like obstacles in the image and
recovering the image. In this paper, rather than using a common RGB camera, we propose a …

A novel dictionary learning (DL) method is proposed to estimate sparse neural activations
from multi-subject fMRI data sets. By exploiting the label information such as the patient and …