Image Restoration Based on a New Regularized Particle Filter

It is known that particle filter has the superior characteristics of solving non-linear non-Gaussian problems. A new regularized particle filter is proposed to improve the quality of image restoration with mixed or multiplicative noise in this paper. To reduce the error of sample importance re-sampling (SIR) particle filter, which comes from the neglect of measuring data when re-sampling, the posterior continuous distribution sample is adopted. The combination of the cumulative distribution function (CDF) and regularized re-sampling step makes the algorithm have the advantages of minimized variance, alleviating degradation and escaping from the exhaustion of particle. The proposed method has been applied to the mixed noisy and medical multiplicative noisy image restoration. The results show the effectiveness of the algorithm, and demonstrate its superiority, compared with wavelet threshold shrink method and SIR particle filter method.