Three Short Stories About Image Denoising
Thursday, October 17, 2019 - 9:05am - 9:50am
This talk describes three recent proposals in the area of patch-based image denoising. The first one is a simple post-processing technique for Poisson denoisers, based on classical linear minimum mean squared error (MMSE) estimation, which is able to squeeze a few extra tenths of dB of ISNR from several state-of-the-art Poisson denoisers and to produce better-looking images. The second part of the talk shows that (external) non-local means (NLM) denoising can be seen as an importance sampling approach to computing MMSE patch estimates, opening the door to using NLM with arbitrary noise models. The third story is about denoising of interferometric (phase) images using multi-resolution windowed Fourier filtering, guided by Stein's unbiased risk estimate (SURE), which outperforms previous state-of-the-art methods for this problem.