Image denoising with an optimal threshold and neighbouring window

Authors:
Zhou Dengwen;Cheng Wengang
Affiliations:
Department of Computer Science and Technology, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China;Department of Computer Science and Technology, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China
Venue:
Pattern Recognition Letters
Year:
2008

Citing 4
Cited 5

Image denoising with neighbour dependency and customized wavelet and threshold

Pattern Recognition
Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising

IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding

IEEE Transactions on Image Processing
The SURE-LET Approach to Image Denoising

IEEE Transactions on Image Processing

Restoration of images corrupted by Gaussian and uniform impulsive noise

Pattern Recognition
Stochastic image denoising based on Markov-chain Monte Carlo sampling

Signal Processing
Image denoising with anisotropic bivariate shrinkage

Signal Processing
Monte Carlo cluster refinement for noise robust image segmentation

Journal of Visual Communication and Image Representation
A versatile denoising method for images contaminated with Gaussian noise

Proceedings of the CUBE International Information Technology Conference

Quantified Score

Hi-index	0.10

Visualization

Abstract

NeighShrink is an efficient image denoising algorithm based on the decimated wavelet transform (DWT). Its disadvantage is to use a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is given, which can determine an optimal threshold and neighbouring window size for every subband by the Stein's unbiased risk estimate (SURE). Its denoising performance is considerably superior to NeighShrink and also outperforms SURE-LET, which is an up-to-date denoising algorithm based on the SURE. It is well known that increasing the redundancy of wavelet transforms can significantly improve the denoising performances. The proposed method is also extended to the redundant dual-tree complex wavelet transform (DT-CWT). Experiments demonstrate that the proposed method on the DT-CWT achieves better results than some of the best denoising algorithms published currently.