Image Restoration using Gaussian Scale Mixtures in Complex Curvelet Transform Domain
In this paper, a complex Curvelet transform is presented at first.The key innovation can be generalized as foliows:2D and ID complex wavelet transform instead á trons algorithm sub-band decomposition and ID wavelet transform respectively, and increase the sampling rate during the 1D IFFT.so the new complex Curvelet transform has non-aliasing performance,and can avoid scratch and embedded stain phenomenon in reconstruction image. On this basis,a new image restoration method using Gaussian scale mixtures in complex Curvelet transform domain is presented. The GSM model can effectively capture the amplitude and phase information of complex Curvelet coefficients.so the degraded complex coefficients modeled can be estimated effectively by using Bayesian least squares(BLS) estimator in order to recover the signal coefficients.Experimental results show that the proposed method can efficiently avoid the problem of noise amplification during the iteration restoration processing .The proposed method has better visual effects,bigger PSNR values than both Wiener restoration method and Lucy-Richardson restoration method.
image restoration compolex Curvelet transform Gaussian scale mixtures Bayesian least squares
Yan He Zhang Xing-lan Li Wei-wei Chen Feng
College of Computer Science,Chongqing University of Technology, Chongqing 400054,China
国际会议
长沙
英文
1552-1555
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）