Contourlet image de-noising based on principal component analysis

Authors:
Li Liu;Jianzheng Dun;Lingfeng Meng
Affiliations:
School of Information Science and Engineering, Lanzhou University, Lanzhou, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 5
Cited 0

De-noising by soft-thresholding

IEEE Transactions on Information Theory
Spatially adaptive wavelet thresholding with context modeling for image denoising

IEEE Transactions on Image Processing
Adaptive wavelet thresholding for image denoising and compression

IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing
Translation-Invariant Contourlet Transform and Its Application to Image Denoising

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new method which utilizes noise energy, instead of noise variance, to perform image de-noising based on Principal Component Analysis in Contourlet domain. The Contourlet transform is a new extension of the wavelet transform that provides a multi-resolution and multidirection analysis for two dimension images. Most of the existing methods for image de-noising rely on accurate estimation of noise variance. However, the estimation of noise variance is difficult in Contourlet domain. The novelty of this method is that it does not rely on the estimation of noise variance, therefore it has great value in solving real-world problems. We compared this method with the wavelet hard-thresholding and soft-thresholding methods which are commonly used in image de-noising. The experimental results show that the proposed approach can obtain better visual results and higher PSNR values, especially for the images that include mostly fine textures and contours.