Quantitative evaluation of denoising algorithms without noise-free ground-truth data

In MRI, the quantitative evaluation of denoising methods is often limited due to the lack of noise-free ground-truth data. We show how to still approximate the quality metrics mean squared error (MSE) and peak signal-to-noise-ratio (PSNR) without access to ground-truth data by using Stein’s unbiased risk estimator (SURE). The proposed method can be employed to evaluate learning- and non-learning-based denoising approaches, assuming an additive Gaussian noise model with known distribution. Our experiments further reveal that the accuracy of our evaluation method increases with the number of test samples available.