基于小波域分类隐马尔可夫树模型的图像恢复

Image Restoration Based on Wavelet-Domain Classified Hidden Markov Tree Model

  • 摘要: 针对自然图像的非平稳特性和图像恢复中计算困难的问题,提出了一种基于小波域分类隐马尔可夫树(CHMT)模型的图像恢复算法.从图像恢复的贝叶斯框架出发,将CHMT模型作为自然图像小波域的先验知识,构造正则化约束进行图像恢复.该模型具有空间适应性,使建模更加精确.对恢复方程的求解,采用了分类简化的共轭梯度算法.实验结果表明,该算法具有较低的计算复杂度,能提高图像恢复峰值信噪比(PSNR).

     

    Abstract: Faced with the non-stationary property of real-world images and the complexity of computation problem in image recovery,an image restoration method using wavelet-domain classified hidden Markov tree(CHMT) model is proposed.According to Bayesian theory of image restoration algorithm,CHMT model is used as a priori information of image in the wavelet-domain,and regularization restriction is made to recover the image.The CHMT model has spatial adaptability,making the modeling more accurate.The restoration equation is solved with the simplified conjugate gradient method.Experimental results showed that this algorithm has a reasonable computational complexity,and the image recovery PSNR is improved.

     

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