Non Linear Image Restoration in Spatial Domain

In the present work, a novel image restoration method from noisy data samples is presented. The restoration was performed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain. Unlike most existing techniques, this approach does not require prior modelling of either the image or noise statistics. The proposed method works in an interactive mode to find the best compromise between the data (mean square error) and the smoothing criteria. The method has been compared with the shrinkage approach, Wiener filter and Non Local Means algorithm as well. Experimental results showed that the proposed method gives better signal to noise ratio as compared to the previously proposed denoising solutions. Furthermore, in addition to the white Gaussian noise, the effectiveness of the proposed technique has also been proved in the presence of multiplicative noise.