Automated Filter Parameter Selection Using Measures of Noiseness

Despite the vast body of literature on image denoising, relatively little work has been done in the area of automatically choosing the filter parameters that yield optimal filter performance. The choice of these parameters is crucial for the performance of any filter. In the literature, some independence-based criteria have been proposed, which measure the degree of independence between the denoised image and the residual image (defined as the difference between the noisy image and the denoised one). We contribute to these criteria and point out an important deficiency inherent in all of them. We also propose a new criterion which quantifies the inherent `noiseness' of the residual image without referring to the denoised image, starting with the assumption of an additive and i.i.d. noise model, with a loose lower bound on the noise variance. Several empirical results are demonstrated on two well-known algorithms: NL-means and total variation, on a database of 13 images at six different noise levels, and for three types of noise distributions.