摘要
针对迭代盲反卷积算法中解的唯一性和收敛性不确定问题,提出了基于小波的迭代盲图像恢复新算法.该算法用基于奇异值分解和压缩滤波方法对降质图像进行预处理,在初始化估计后对图像进行小波变换,用局部高斯模型刻画图像小波系数的概率分布,并作为先验约束加入到迭代算法中.仿真结果表明,新算法在保持图像边缘特性的基础上,峰值信噪比改进量比原算法增加约3 dB,对解的唯一性和收敛性也有一定改善.
To cope with the uncertainty of the solution＇ s uniqueness and convergence in the iterative blind deconvolution（IBD） method, a novel wavelet-based iterative blind image restoration algorithm is proposed. A singular value decomposition（SVD） and compression-based filtering method is used for pretreatment, discrete wavelet transform（DWT） is made after the initial image estimate, and local Gaussian model （LGM） is used as a priori restriction added to the IBD method. Simulation results showed that, the combination of these techniques can perform better about 3 dB （PSNR） than the original IBD method in recovering images, with edge information well reserved. At the same time , the uniqueness and convergence properties of IBD are improved.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2006年第9期828-830,835,共4页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(60472110)
关键词
迭代盲反卷积
盲图像恢复
小波变换
奇异值分解
iterative blind deeonvolution
blind image restoration
wavelet transform
singular value decomposition
