Image denoising using neighbor and level dependency

Authors:
Dongwook Cho;Tien D. Bui;Guangyi Chen
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada;Department of Computer Science, McGill University, Montréal, Québec, Canada
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 5
Cited 0

Wavelet-based statistical signal processing using hidden Markovmodels

IEEE Transactions on Signal Processing
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
Image denoising using scale mixtures of Gaussians in the wavelet domain

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we present new wavelet shrinkage methods for image denoising. The methods take advantage of the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. We also investigate a multiplying factor for the universal threshold in order to obtain better denoising results. An empirical study of this factor shows that its optimum value is approximately the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), require less computation time and produce less visual artifacts compared to other methods.