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Indexed by:期刊论文

Date of Publication:2021-02-02

Journal:IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Volume:42

Issue:12

Page Number:3027-3039

ISSN No.:0162-8828

Key Words:Inverse problems; Convergence; Iterative methods; Learning systems; Acceleration; Iterative algorithms; Learning systems; Statistical analysis; Nonconvex optimization; learning-based iteration; convergence guarantee; image deconvolution; rain streaks removal

Abstract:Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.