基于小波域HMT模型的图像复原

Image Restoration Based on Wavelet-Domain Hidden Markov Tree Model

小波域隐马尔可夫树(HMT)模型采用混合高斯模型刻画各子带系数的概率分布,并通过小波系数隐状态在多个尺度之间的Markov依赖性来刻画自然图像小波系数随尺度呈指数衰减的特性.提出了基于小波域HMT模型的图像复原算法作为自然图像的先验模型,将图像复原问题转化为一个约束优化问题并用共轭梯度法对其进行求解.实验结果表明,该算法较好地再现了各种边缘信息,复原出的图像在信噪比和视觉效果方面都有显著的提高.

Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of the wavelet coefficients,whose distribution densities can be approximated by the Gaussian mixture model.The algorithm presented in this paper specifies the prior distribution of the real-world image through wavelet-domain HMT model and converts the restoration problem to an constrained optimization one which can be solved with the conjugate gradient method.Experimental results show that the algorithm properly retrieves various kinds of edges and the PSNR and subjective visual effect of the restored images are improved significantly.