Abstract
Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrodinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.