Mário Figueiredo,“Three Recent Short Stories About Image Denoising”
Mário Figueiredo, PhD
Distinguished Professor
University of Lisbon
Portugal
“Three Recent 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.
Biography: Mário Figueiredo is a Distinguished Professor at Instituto Superior Técnico, University of Lisbon, with the Department of Electrical and Computer Engineering. He is also a senior researcher and group leader at Instituto de Telecomunicações. His research areas include machine learning, signal processing, and optimization. He has received several honors and awards, namely: Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Fellow of the International Association for Pattern Recognition (IAPR), Fellow of the European Association for Signal Processing (EURASIP), IEEE Signal Processing Society Best Paper Award, W. R. G. Baker Award (IEEE), Pierre Devijver Award (IAPR), Technical Achievement Award (EURASIP), member of the Portuguese Academy of Engineering, member of the Lisbon Academy of Science. From 2014 to 2018 he was included in the annual list “Highly Cited Researchers.”
Topic: MINDS & CIS Fall Seminar Series
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