基于小波收缩和正逆扩散结合的优质中值先验图像重建算法

High quality median prior image reconstruction algorithm based on wavelet shrinkage and forward-and-backward diffusion

针对最大后验(MAP)法对重建图像造成的过度平滑或出现阶梯状边缘伪影等问题,提出了一种基于混合模型的中值先验图像重建算法。首先在中值先验分布的MAP重建的基础上,在每次中值滤波之前引入结合小波收缩和正逆各向异性扩散的滤波器。另外,对于背景区域仍残留有少量噪声的情况下,可以在迭代间的最后,选择加入只针对图像较小梯度阈值区域进行非线性扩散平滑的优良滤波器,从而进一步优化图像。仿真结果表明,该算法在抑制噪声和保持边缘效果方面具有很好的表现,与其他经典传统算法相比,信噪比(SNR)可提高0.9 dB～3.8 dB。

A median priori image reconstruction algorithm based on mixed model was put forward to solve the problems of over-smoothness and stepladder edge of reconstructed image by Maximum A Posterior（MAP）.First,in the median priori distribution of MAP reconstruction method,the combination of wavelet shrinkage and forward-and-backward anisotropic diffusion filter was introduced before each of median filtering.In addition,if the background area still kept a small amount of noise,the fine filter with a nonlinear diffusion that smoothed the smaller image gradient threshold region could be chosen to join in the last of iteration,so as to optimize the image.The simulation results show that the algorithm has good performance in both lowering noise effect and preserving edges.Compared with other classical algorithms,the Signal-to-Noise Ratio（SNR） can be improved by 0.9 dB to 3.8 dB.