Fast and Efficient Method for Image Denoising

This paper presents a fast and efficient RaoBlackwellized Particle Filter (RBPF) for real noisy image restoration. The proposed method first estimates the noise level from the noisy image. Then RBPF with Maximum Likelihood Estimation method is used for noise removal. The Maximum likelihood Estimation method is used for noise distribution process. Rao-Blackwellized particle filtering is a combination of a particle filter (PF) and a bank of Kalman filters. The distribution of the discrete states is computed by using PF and the distribution of the continuous states are computed by using a bank of Kalman filters. An accurate proposal distribution is computed by using conditionally Gaussian state space models and Rao-Blackwellized particle filtering. The performance of the method is improved by parallel pixel processing. This algorithm, which exhibits good performance both in denoising and in restoration, can be easily and effectively parallelized. Experimental results carried out with real noisy satellite images. The RBPF is very effective in eliminating noise. RBPF is compared with other standard filters. In terms of noise removal and performance RBPF outperforms for naturally degraded satellite images.