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

**Authors:** Rowayda A. Sadek  
**arXiv:** [1211.7102](https://arxiv.org/abs/1211.7102)  
**Date:** 2012-11-29  
**Summary page:** https://www.summarizepaper.com/en/paper/1211.7102v1/

## Key points

- The paper presents an experimental survey on the use of Singular Value Decomposition (SVD) as an efficient transform in image processing applications.
- SVD has emerged as a new paradigm for processing different types of images due to its attractive algebraic properties.
- The paper aims to explore the generous properties of SVD and utilize them in developing new SVD-based image processing applications.
- It experimentally presents the properties of SVD for images and proposes some new contributions that were originated from SVD properties analysis in different image processing.
- It offers a survey on the developed SVD based image applications.
- Despite being used extensively in other fields, there are still many unexplored areas where SVD can be applied to enhance image quality or extract useful information from images.
- The authors recommend further research challenges to fully exploit the potential of using SVD-based techniques for various imaging applications.

## Summary

The paper "SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges" by Rowayda A. Sadek presents an experimental survey on the use of Singular Value Decomposition (SVD) as an efficient transform in image processing applications. SVD has recently emerged as a new paradigm for processing different types of images due to its attractive algebraic properties. Despite the fact that SVD offers many advantages in imaging, its potential for various image applications is still at its infancy. Therefore, this paper aims to explore the generous properties of SVD and utilize them in developing new SVD-based image processing applications. The paper experimentally presents the properties of SVD for images and proposes some new contributions that were originated from SVD properties analysis in different image processing. It also offers a survey on the developed SVD based image applications. 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 future research. The paper highlights that despite being used extensively in other fields such as signal processing, data compression, and machine learning, there are still many unexplored areas where SVD can be applied to enhance image quality or extract useful information from images. The authors recommend further research challenges to fully exploit the potential of using SVD-based techniques for various imaging applications. In conclusion, this paper provides valuable insights into using Singular Value Decomposition (SVD) as an efficient transform for developing new image processing applications. It highlights the underutilized potential of using SVD-based techniques for enhancing image quality or extracting useful information from images and recommends further research challenges to fully exploit these opportunities.