Learning denoising bounds for noisy images

In [1], we derived an expression for the fundamental limit to image denoising assuming that the noise-free image is available. In this paper, we propose an estimator for the bound on the mean squared error given only the noisy image and noise characteristics. To do this, we make use of an assortment of independently collected noise-free images from which prior information about the noisy image is learned. We show that even for reasonably low input signal-to-noise levels, our method can predict the denoising bound with accuracy.