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Singular Value Decomposition (SVD) has recently emerged as a new paradigm forprocessing different types of images. SVD is an attractive algebraic transformfor image processing applications. The paper proposes an experimental surveyfor the SVD as an efficient transform in image processing applications. Despitethe well-known fact that SVD offers attractive properties in imaging, theexploring of using its properties in various image applications is currently atits infancy. Since the SVD has many attractive properties have not beenutilized, this paper contributes in using these generous properties in newlyimage applications and gives a highly recommendation for more researchchallenges. In this paper, the SVD properties for images are experimentallypresented to be utilized in developing new SVD-based image processingapplications. The paper offers survey on the developed SVD based imageapplications. The paper also proposes some new contributions that wereoriginated from SVD properties analysis in different image processing. The aimof this paper is to provide a better understanding of the SVD in imageprocessing and identify important various applications and open researchdirections in this increasingly important area; SVD based image processing inthe future research.

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