The singular value decomposition (SVD) is a generalization of the eigen-decomposition which can be used to analyze rectangular matrices (the eigen-decomposition is definedonly for squaredmatrices). By analogy with the eigen-decomposition, which decomposes a matrix into two simple matrices, the main idea of the SVD is to decompose a rectangular matrix into three simple matrices: Two orthogonal m...

In this paper, a novel contrast enhancement technique for contrast enhancement of a low-contrast satellite image has been proposed based on the singular value decomposition (SVD) and discrete cosine transform (DCT). The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed techniq...

The different orthogonal relationship that exists in the Löwdin orthogonalizat ions is presented. Other orthogonalizat ion techniques such as polar decomposition (PD), principal component analysis (PCA) and reduced singular value decomposition (SVD) can be derived from Löwdin methods. It is analytically shown that the polar decomposition is presented in the symmetric o rthogonalization; princip...

In this paper, a color image watermarking algorithm based on Redundant Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) is proposed. The new algorithm selects blue component of a color image to carry the watermark information since the Human Visual System (HVS) is least sensitive to it. To increase the robustness especially towards affine attacks, RDWT is adopted for its...

We explore the use of the singular value decomposition (SVD) in image compression. We link the SVD and the multiresolution algorithms. In [22] it is derived a multiresolution representation of the SVD decomposition, and in [15] the SVD algorithm and Wavelets are linked, proposing a mixed algorithm which roughly consist on applying firstly a discrete Wavelet transform and secondly the SVD algori...

This note is intended to summarize the definition, properties, interpretation and applications of Singular Value Decomposition (SVD) described by Strang [?], Shilov [?], Johnson and Wichern [?] and Will [?]. 1 Definition Singular Value Decomposition (SVD) technique decomposes a m × n matrix A into the following factored form :

Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. In this work we introduce a new approach to improve the preciseness of the standard Quantum Fourier Transform. The presented Quantum-SVD algorithm is based on the singular value dec...

In this paper, we have implemented singular value decomposition to effectively update the decomposition, including the basis images. We will use two dimensional discrete wavelet transform (2D-DWT) and singular value decomposition (SVD). Hybrid method with SVD and DWT will help us to store the images with less storage requirements and will keep the level of the error that must be acceptable in a...

Watermarking algorithms of digital images based on singular value decomposition (SVD) have been proposed recently. Most SVD-based watermarking techniques use singular values as the embedding watermark information. These SVD-based techniques are advantageous for watermarking images since slight changes in the singular values do not affect significantly