Image denoising is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. As expected, the era of deep learning has brought yet another revolution to this subfield, and took the lead in today’s ability for noise suppression in images. This talk focuses on recently discovered abilities and opportunities of image denoisers. We expose the possibility of using image denoisers for serving other problems, such as regularizing general inverse problems and serving as the engine for image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have you believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems.
This is an invited talk, given in the “A Multiscale tour of Harmonic Analysis and Machine Learning” event, on April 19-21, Celebrating Stéphane Mallat’s 60th birthday.
Recordings of all the talks in this event can be found here.