基于马尔可夫随机场的快速乘性迭代盲去卷积

提出了一种乘性迭代盲图像去卷积算法,该算法假定被估计的图像与PSF满足MRF模型.除了自动地保持非负性约束外,算法引入了一组新的具有抗噪功能的差分算子,该算子首先加权求和然后进行差分运算.交替最小化方法被用来将盲图像去卷积这一非线性反问题线性分解为交替的图像恢复步与PSF估计步.在交替迭代过程中,考虑到算法收敛较慢,一种称为向量外推的加速方法被采用.数值实验结果表明,本算法能快速地实现对降质的中巴地球资源卫星02B星高分辨率图像的复原,同时有效地保存了图像的细节边缘并抑制了噪声.

Fast Multiplicative Iterative Blind Deconvolution Based on Markov Random Field

A multiplicative iterative blind image deconvolution algorithm was proposed, in which both estimated image and Point Spread Function (PSF) were assumed to follow Markov Random Field (MRF) models. In addition to natural preservation of non-negative constraints, the algorithm introduced a set of novel antinoise difference operators, which first carried out weighted summation and then difference. Alternating Minimization (AM) method was adopted to linearly decompose the blind image deconvolution that belonged to a class of nonlinear inverse problems into alternate image restoration step and PSF estimation step. During the alternate iteration, an acceleration method called as vector extrapolation was applied to the alternate iteration steps owing to slow convergence of the algorithm. Numerical experiment results show the proposed algorithm can rapidly realize the restoration of the degraded High Resolution (HR) image from China & Brazil earth resource satellite-02B (CBERS-02B) meanwhile effectively preserves detailed edges and inhibits noise.