Three Short Stories About Image Denoising

Abstract: In this talk, I will review 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 an elementary application of classical linear minimum mean squared error (LMMSE) estimation, and 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 story is about how external non-local means (NLM) denoising can be seen as an importance sampling approach to computing MMSE patch estimates, which opens the door to using NLM with arbitrary noise models. The third and final part of the talk is about denoising interferometric (phase) images using multi-resolution windowed Fourier filtering, guided by Stein’s unbiased risk estimate (SURE), outperforming previous state-of-the-art methods for this problem.