SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

TL;DR

This paper surveys the use of Singular Value Decomposition (SVD) in image processing, highlighting its properties, current applications, and future research challenges to advance the field.

Contribution

It provides an experimental analysis of SVD properties for images, surveys existing applications, and proposes new research directions leveraging SVD's potential.

Findings

SVD offers valuable properties for image processing.

Current applications of SVD in imaging are limited but promising.

Identifies open research challenges and future directions in SVD-based image processing.

Abstract

Singular Value Decomposition (SVD) has recently emerged as a new paradigm for processing different types of images. SVD is an attractive algebraic transform for image processing applications. The paper proposes an experimental survey for the SVD as an efficient transform in image processing applications. Despite the well-known fact that SVD offers attractive properties in imaging, the exploring of using its properties in various image applications is currently at its infancy. Since the SVD has many attractive properties have not been utilized, this paper contributes in using these generous properties in newly image applications and gives a highly recommendation for more research challenges. In this paper, the SVD properties for images are experimentally presented to be utilized in developing new SVD-based image processing applications. The paper offers survey on the developed SVD based image applications. The paper also proposes some new contributions that were originated from SVD properties analysis in different image processing. The aim of this paper is to provide a better understanding of the SVD in image processing and identify important various applications and open research directions in this increasingly important area; SVD based image processing in the future research.