2012
DOI: 10.14569/ijacsa.2012.030703
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SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Abstract: 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 at… Show more

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Cited by 73 publications
(8 citation statements)
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“…The condition number depends on the properties of a particular integral operator and can indeed be large in many applications such as image processing [14][15][16][17], geophysics [18][19][20][21] and high energy physics [22][23][24][25][26]. However, as it is shown in section 2, the condition vector norms are used in eq.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…The condition number depends on the properties of a particular integral operator and can indeed be large in many applications such as image processing [14][15][16][17], geophysics [18][19][20][21] and high energy physics [22][23][24][25][26]. However, as it is shown in section 2, the condition vector norms are used in eq.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…Whilst it may seem trivial to solve this equation, the inverse of a nonsquare matrix is non-existent (which is true for almost all the cases, since total number of single-speckle pixels is much greater than the number of captured speckles). The solution to this computation problem is to use the Moore-Penrose pseudo-inverse of the matrix C, which can be calculated from the singular value decomposition: [16][17][18] U and V are unitary matrices, Σ is a diagonal matrix with the singular values and T denotes the matrix transpose. Matrix Σ −1 is obtained by reciprocating the main diagonal of the Σ matrix (singular values) and then transposing the resulted matrix.…”
Section: Strain Measurementmentioning
confidence: 99%
“…Angle measurement is obtained using singular value decomposition (SVD) and principal component analysis (PCA) as described elsewhere. 1,3,[5][6][7][8] Initially, the system was used to measure polarization rotation using a 1050 nm laser with 20 pm linewidth beam which was linearly polarized through a Glan-Thompson polarizer (Thorlabs, GTH10M) and a half waveplate (Thorlabs, WPHSM05-1064) (Fig. 2a).…”
Section: Polarization Angle Detectionmentioning
confidence: 99%