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

Abstract

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.