Section: New Results

Emerging activities on Nonlinear Inverse Problems

Compressive sensing, compressive learning, audio inpainting, phase estimation

Audio Inpainting and Denoising

Participants : Rémi Gribonval, Nancy Bertin, Clément Gaultier.

Main collaborations: Srdan Kitic (Orange, Rennes)

Inpainting is a particular kind of inverse problems that has been extensively addressed in the recent years in the field of image processing. Building upon our previous pioneering contributions [57], we proposed over the last five years a series of algorithms leveraging the competitive cosparse approach, which offers a very appealing trade-off between reconstruction performance and computational time, and its extensions to the incorporation of the so-called “social” into problems regularized by a cosparse prior. We exhibited a common framework allowing to tackle both denoising and declipping in a unified fashion [69]; these results, together with listening tests results that were specified and prepared in 2019 and will be run soon, will be included in an ongoing journal paper, to be submitted in 2020. This year, following Clément Gaultier Ph.D. defense [12], we progressed towards industrial transfer of these results through informal interaction with a company commercializing audio plugins, in particular with new developments to alleviate some artifacts absent from simulation but arising in real-world use cases.