University of Birmingham > Talks@bham > Optimisation and Numerical Analysis Seminars > Provably Convergent Plug-and-Play Quasi-Newton Methods for Imaging Inverse Problems

Provably Convergent Plug-and-Play Quasi-Newton Methods for Imaging Inverse Problems

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  • UserJunqi Tang (University of Birmingham, School of Mathematics)
  • ClockTuesday 13 June 2023, 14:00-15:00
  • HouseVenue to be confirmed.

If you have a question about this talk, please contact Sergey Sergeev.

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM . Existing provable PnP methods impose heavy restrictions on the denoiser or fidelity function, such as nonexpansiveness or strict convexity. In this work, we propose a provable PnP method that imposes relatively light conditions based on proximal denoisers, and introduce a quasi-Newton step to greatly accelerate convergence. By specially parameterizing the deep denoiser as a gradient step, we further characterize the fixed-points of the quasi-Newton PnP algorithm. We demonstrate the effectiveness of our scheme in applications such as image deblurring, inpainting and superresolution, observing order-of-magnitude acceleration over state-of-the-art PnP algorithms.

This talk is part of the Optimisation and Numerical Analysis Seminars series.

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