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Mathematical Problems in Engineering
Volume 2015, Article ID 469350, 9 pages
Research Article

An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

1Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
2School of Information Engineering, Jiangsu Maritime Institute, Nanjing 211100, China
3Nanjing College of Information Technology, Nanjing 210023, China
4Department of Control and Systems Engineering, Nanjing University, Nanjing 210093, China

Received 26 July 2015; Revised 22 October 2015; Accepted 2 November 2015

Academic Editor: Yann Favennec

Copyright © 2015 Min Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.