A New Approach for Denoising Images Based on Weights Optimization
Résumé
We propose a new algorithm to restore an image contaminated by the Gaussian white noise. Our approach is based on the weighted average of the observations in a neighborhood as in the case of the Non-Local Means Filter. But in contrast to the Non-Local Means Filter, we choose the weights by minimizing a tight upper bound of the Mean Square Error. Our theoretical results show that some ”oracle” weights defined by a triangular kernel are optimal. To construct a computable filter the ”oracle” weights are replaced by some estimates. The implementation of the proposed algorithm is straightforward. The simulations show that our approach is very competitive.