Title :
The effect of image rotation on UTV decomposition
Author_Institution :
Dept. of Biomed. Eng., Mahidol Univ., Nakhonpathom
Abstract :
Since the singular value decomposition (SVD) consumes high computational complexity on updating its eigenvectors and eigenvalues when new data are included, an alternate rank-revealing orthogonal decomposition that can eliminate this problem such as the UTV decomposition is one of our particular interest. This paper presents a study on directions of principal structures of the images and their effects when the UTV decomposition is employed. The relationship between the UTV decomposition and SVD is also explored. The proposed image denoising algorithm illustrates that the UTV decomposition can efficiently decompose images with vertical/horizontal structures into only a few component as well as the SVD.
Keywords :
computational complexity; image denoising; singular value decomposition; SVD; UTV decomposition; computational complexity; image denoising algorithm; image rotation; rank-revealing orthogonal decomposition; singular value decomposition; vertical-horizontal structures; Analytical models; Biomedical engineering; Computational complexity; Eigenvalues and eigenfunctions; Equations; Image denoising; Mathematical analysis; Matrix decomposition; Noise reduction; Singular value decomposition;
Conference_Titel :
Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1723-0
Electronic_ISBN :
978-1-4244-1724-7
DOI :
10.1109/ICALIP.2008.4590114