Image Denoising Based on the Sparse Land Using Redundant Bandelet Transform
Huaixin Chen · Journal of the China Railway Society · 2010
To improve the efficiency of image denoising at the high Gaussian white noise level,a novel scheme is proposed by combining with the second redundant Bandelet transform version based on the Sparse Land-Basis Pursuit.Starting from the interrelation between Basis Pursuit denoising and threshold denoising with the shrinkage method,the Lagrangian cost function is renewed to minimize the influence of noises and have clearer meanings,and also lead to reduction of computation complexity.There are three steps in the image denoising process.Firstly,translation of invariant 2D wavelets is used to obtain the redundant Bandelet transform version.Secondly,during finding the best geometrical flow and optimal quadtree segments,the cost term of Lagrangian is confirmed by the Bayes estimator.Thirdly,Bayes soft-threshold shrinkage denoising in the bandelet transform domain is implemented.This leads to the state-of-the-art denoising performance,equivalent and sometimes surpassing recently published leading alternative denoising methods,especially as the noise variance is equal to and larger than 502.