A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the KolmogorovSmirnov test
An NLML method for denoising MRI based on Kolmogorov-Smirnov (KS) similarity test is proposed. The proposed method is statistically convincing and performs better than the conventional NLML method. Through the proposed approach the samples for ML estimation can be selected in an adaptive way. Quantitative analysis at various noise levels based on the various similarity measures, shows that the proposed method is more effective than conventional NLML.
non-local maximum likelihood noise kolmogorovsmirnov test denoising mri non-central chi-square distribution