核心概念
The authors propose innovative methods for nonconvex optimization using extrapolated splitting techniques and denoising priors, showcasing improved convergence and performance in image restoration applications.
要約
This paper introduces novel extrapolated DYS methods for nonconvex optimization problems, integrating acceleration techniques and deep learning-based denoisers. The convergence properties are rigorously analyzed based on the Kurdyka-Lojasiewicz property, demonstrating superior performance in image restoration tasks.
Key points include:
- Introduction of extrapolated DYS method for nonconvex optimization problems.
- Incorporation of deep learning-based denoisers in PnP-DYS algorithms.
- Extensive experiments showcasing advantages of the proposed schemes in image deblurring and super-resolution.
- Convergence analysis based on subdifferentials and KL property.
- Parameter conditions ensuring convergence and sublinear convergence rates.
The content delves into advanced mathematical concepts applied to real-world image processing challenges, offering a comprehensive approach to nonconvex optimization with practical implications.
統計
The parameters α and γ should be chosen such that 0 < γ < 1/(Lf1+Lh) and 0 ≤ α < Λ(γ).
For given Lf1 > 0 and Lh ≥ 0, Λ(γ) > 0 always holds if γ > 0 is sufficiently small.