This technique, rooted in
3 Definition of the generalized singular value decomposition of the tensor
To understand the concept of Singular Value Decomposition the knowledge on eigenvalues and eigenvectors is
During last decades, singular value decomposition has been widely used in different fields of engineering and science
On the other hand, in more recent years, the quaternion has proven to be a very suitable tool for color pixel representation as it can
The singular value decomposition (SVD) method is
Suppose we have a singular value decomposition A = UΣVT where Σ = [σ1 0 0 0 0 0 σ2 0 0 0 0 0 σ3 0 0 0 0 0 0 0]
Here, combining image priors, a two-stage
The advantage of using the SVD is the property of energy compaction and its ability to adapt to the local statistical variations of an image
More specifically, we focus on image compression and matrix completion
The Singular Value Decomposition (SVD) is a highlight of linear algebra and has a wide range application in computer vision, statistics and machine learning
The new algorithm represents a color image as an RQM and handles such an image in a holistic manner